Comparing the mechanical properties of the printed models with pure silicone models.
\r\n\tThus, we call for research and review papers on the chemistry and physics of dyes, pigments, and their intermediates, including chemical constituents, spectroscopic aspects, surface, solution, crystal formation, photochemical, and ecological or biological properties. The book will be of interest to a wide variety of researchers worldwide whose work involves various fields of activity such as dyes and pigment synthesis, imaging, sensor, energy, medicine, polymers, food product, toxicological properties, etc.
",isbn:"978-1-83768-114-3",printIsbn:"978-1-83768-113-6",pdfIsbn:"978-1-83768-115-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"fcd069956c2e931195925b19a74ce9a3",bookSignature:"Dr. Brajesh Kumar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12081.jpg",keywords:"Heterocycles Pigments, Azo, Nitro, Indigo, Alizarin, Chromophores, Chromophores, Photochemical, Sulphonation, Diazotisation, UV-Vis Spectroscopy, Metal-Ligand",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 19th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"20 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Brajesh Kumar has worked as a faculty member in various universities in India, Ecuador, and South Korea. He has published numerous SCI/SCIE/Scopus research articles and is an active reviewer of more than 50 Journals. Dr. Kumar is a member of the American Chemical Society, the Indian Society of Chemists and Biologists, and the Indian Science Congress Association and holder of two registered patents. He is included in the top 2% of the scientist list prepared by experts at Stanford University,",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"176093",title:"Dr.",name:"Brajesh",middleName:null,surname:"Kumar",slug:"brajesh-kumar",fullName:"Brajesh Kumar",profilePictureURL:"https://mts.intechopen.com/storage/users/176093/images/system/176093.JPG",biography:"Dr. Brajesh Kumar is currently working as an Assistant Professor and Head in the Post Graduate Department of Chemistry, TATA College, Chaibasa, India. He received a Ph.D. in Chemistry from the University of Delhi, India. His research interest is in the development of sustainable and eco-friendly techniques for (a) nanoparticles synthesis and their applications for environmental remediation, (b) active films of organic solar cells, (c) nanomedicine, (d) sensors, (e) natural product extraction, purification, and analysis,(f) natural polymers, (g) peptide chemistry, (h) microwave and ultrasound-assisted organic synthesis and (i) organic synthesis. Dr. Brajesh Kumar has been credited for different national and international fellowships and he has also worked as a faculty member in various universities of India, Ecuador, and South Korea. He has also published numerous SCI/ SCIE/ Scopus research articles (h index = 28, Citations 2690) and is also an active reviewer of more than 50 Journals. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3828",title:"Application of Nanotechnology in Drug Delivery",subtitle:null,isOpenForSubmission:!1,hash:"51a27e7adbfafcfedb6e9683f209cba4",slug:"application-of-nanotechnology-in-drug-delivery",bookSignature:"Ali Demir Sezer",coverURL:"https://cdn.intechopen.com/books/images_new/3828.jpg",editedByType:"Edited by",editors:[{id:"62389",title:"PhD.",name:"Ali Demir",surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3569",title:"Biodegradation",subtitle:"Life of Science",isOpenForSubmission:!1,hash:"bb737eb528a53e5106c7e218d5f12ec6",slug:"biodegradation-life-of-science",bookSignature:"Rolando Chamy and Francisca Rosenkranz",coverURL:"https://cdn.intechopen.com/books/images_new/3569.jpg",editedByType:"Edited by",editors:[{id:"165784",title:"Dr.",name:"Rolando",surname:"Chamy",slug:"rolando-chamy",fullName:"Rolando Chamy"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"67072",title:"Optimization of Maxillofacial Prosthesis",doi:"10.5772/intechopen.85034",slug:"optimization-of-maxillofacial-prosthesis",body:'Anaplastology is a multidisciplinary branch of medicine that deals with artificial reconstruction of a disfigured, absent or anatomically malformed part of the face or body by fabricating a customized facial or somatic prosthesis for the patient [1]. The prostheses provide descriptive evidence for steps of fabrication of these devices, including location, retention, support, time, materials, and form [2, 3]. Prostheses are artificial devices which either implanted or attached to the body to replace or restore a body part that might be congenitally missing or might have been lost due to tumor ablation or external trauma [4]. Facial disfiguration is considered a challenge for the patient; as it negatively interferes with the patient’s self-image and ability coexist in a normal social life. Although the prosthesis is well appreciated by the patients, however, in many instances it does not restore function totally [1, 5]. Surgery can repair small defects, whereas, large defects could not be repaired surgically [6], Hence, prosthetic rehabilitation is frequently applied. This depends on a variety of factors including patient’s age and systemic condition, size and site of the defect, patient’s satisfaction and cost factors [7, 8, 9]. For example, an old patient with poor systemic health is not a good candidate for surgery, on the other hand, an impaired vision or a poor manual dexterity patient is not a good candidate for prosthesis as he will not be able to maintain the prosthesis properly.
Defects in the craniofacial region mostly lead to severe depression, even in some instances to self-isolation and rejection of life, hence, surgical reconstruction and/or prosthetic devises will be an insistent demand for a patient with facial disfiguration [10]. Esthetically appropriate Prosthetic rehabilitation of the patient is rather challenging requires multidisciplinary team for comprehensive care and optimal cost treatment functional and esthetic outcomes [11, 12, 13, 14]. Oro-facial areas comprises a variety of vital and important structures, every so often surgical management of cancer in this region predominantly with widespread cancerous lesion require extensive removal of tissue—the cancerous lesion and part from the normal tissue around the lesion as a protective measure of surgical management of cancer. As a result of this aggressive surgical procedure many vital functions would be impaired such as esthetics, phonetics, mastication and vision. In these cases an extensive defect would be left behind that would most probably not be reconstructed surgically, alternatively prosthetic rehabilitation will be performed to improve patient’s esthetics/function [15, 16]. Prosthetic rehabilitation of these patient provides comfort to the patients, improves their confidence and self-esteem. High level of satisfaction was recorded among patients wearing facial prostheses [17]. They experienced much better quality of life after wearing facial prostheses [16, 18].
The fabrication protocol of facial prostheses involves several intricate steps as described by many authors [19, 20, 21] including taking an impression or impressions, obtaining an accurate stone cast in order to carve an accurate wax model for the defect on that cast. The wax model then checked on the patient and transferred to the final material, which is mostly be a silicone polymers by process of flasking and deflasking after adding the basic skin color. Ultimate color matching is accomplished by adding extrinsic colors at the time of fitting and delivery.
Method of fabrication that is applied currently has shown several limitations. These are primarily related to the fabrication protocol, high technical expertise required, time, effort, cost plus retention and esthetic problems. These limitations make access to global patient’s community almost denied, only a small number of these patients can get access to this sophisticated device, those who can afford the high cost of the prosthesis, whereas, people at the other poor global regions such as Africa and India they cannot easily obtain a good prosthesis.
In recent years, both additive manufacturing (also known as 3D printing) and 3D image technology had been developed dramatically and becomes more and more popular in medical science under the term of medical rapid prototyping (MRP). Medical Rapid prototyping was first described by Mankowich et al. in 1990 for imaging and producing anatomically accurate human parts models by rapid prototyping methods [22]. MRP then started to grow more and more to involve a wide range and fields in medicine including tissue engineering, dental implantology, craniofacial surgery and reconstruction and orthopedics.
Many aspects of this brilliant technology have still not been entirely functional for maxillofacial surgical/prosthetic rehabilitation. This technology has not been fully incorporated in producing maxillofacial soft tissue prostheses. However, some articles and few case reports applied this technology in the manufacturing process as producing accurate wax models for ear and other parts of the face using 3D printing machines to be replicated by the silicone polymers [23, 24, 25, 26, 27]. They were able to produce highly accurate anatomical models of the missing parts, nevertheless, the entire procedure found to become more time consuming and much costly than if the prosthesis made by hand alone.
In our previous studies, an innovated method of fabrication of soft tissue facial prostheses using 3D color printing technology have been developed using Z-Corp printer, printing in starch as a powder and colored ink as a water based binder, printing process based on computer aided design and manufacture CAD/CAM [28, 29, 30]. Figure 1 summarize the current project that starts with 3D Data acquisition instead of using a complicated multiple impression techniques, then processing these date in a 3D computer aided design—CAD package, building a virtual 3D model for the prosthesis, color mapping then the printing process accomplished using Z510-3D color printer. After printing the robot models infiltrated with elastomeric silicone in order to achieve skin texture and softness. Furthermore, data can be saved for future printing of further copies on demand.
An overview of rapid manufacturing technology applied to fabricate soft tissue facial prostheses.
With above protocol, there is huge potential to replace the conventional technology by the rapid manufacturing technology with saving both time and cost. However, some more factors affect quality of prostheses significantly, including mechanical properties, infiltration and degree of skin color reproduction. In this study, these factors are investigated and further developed. Results are described in following sections.
The mechanical properties of facial prostheses is very important since it directly related to durability of the prostheses. For 3D printing technology we proposed, a starch powder were used to print soft tissue prostheses by a Z-Corp Z510 3D printer and infiltrated using silicone polymers as the post processing. The mechanical properties of the composite produced by Z-Corp printer is tested here by comparing its’ mechanical properties with object produced by silicone polymer using conventional technology [31].
Test models that were printed from starch by Z Corp 3D printer and infiltrated with maxillofacial silicone polymer—Sil-25 are shown in Figure 2.
Starch printed infiltrated silicone test samples for (A) dumbbell-shaped for tensile strength, (B) trouser-shaped for tear strength and (C) hardness test blocks.
Mechanical test for conventional technology is simulated using pure silicone polymers and used as control samples (Figure 3).
Silicone polymers test samples for (A) dumbbell-shaped for tensile strength, (B) hardness test blocks and (C) trouser-shaped for tear strength.
Pure silicone samples were designed according to ASTM specifications for tensile strength (Dumbbell-shaped specimens [32]), tear strength (Trouser-shaped specimens [33]), hardness test [34], and percentage elongation using solid work 2008 software for printing test samples and stainless steel molds were fabricated for the control samples (Figure 4).
Stainless steel molds for fabrication of control samples—pure silicone.
Lloyd LRX tensile instrument applied to test tensile strength, tear strength and percentage elongation (Figure 5).
Lloyd LRX tensile tester testing tensile and tear strength of the printed samples.
Shore Durometer Hardness Tester was applied to test the hardness of the 3D printed starch models infiltrated silicone polymers to be compared with pure silicone samples (Figure 6).
Hardness tester testing hardness of the printed samples.
The collected data was analyzed using PASW statistics 18 to compare between the test group—3D printed samples and control group—pure silicone samples, Independent sample T test was utilized for the statistical analysis.
Table 1, Demonstrating the result of mechanical tests that reveals that test group—the 3D printed samples has significantly lower tensile, tear, and percentage elongation than control samples—pure silicone samples (
Sample | Tensile stress (PSI) | Tear stress (N/mm) | Hardness | Elongation (%) |
---|---|---|---|---|
Silicone (Convectional) | ||||
Average | 455.98 | 10.77 | 30.89 | 480.75 |
SD | 32.20 | 2.60 | 0.71 | 84.40 |
Silicone infiltrated starch (3D printing) | ||||
Average | 170.45 | 8.02 | 62.80 | 221.46 |
SD | 36.10 | 1.68 | 2.782 | 51.44 |
Comparing the mechanical properties of the printed models with pure silicone models.
The results indicated an increased hardness, and consequently the prostheses lose some flexibility, hardness is not the only issue that determine the flexibility, here the technology applied provide shell-like models and the prostheses built according to CAD/CAM is shell prosthesis showing high degree of flexibility than handmade prosthesis despite increased hardness of the printed prostheses compared to pure silicone prostheses as shown in Figure 7.
(A) Method of infiltration leaving feather edged margin of the prosthesis. (B) Flexibility of printed prosthesis infiltrated silicone polymer.
Lower values of tensile, tear strength and percentage elongation do not indicate a critical problem if the patient maintained and handled the prosthesis gently, as a matter of fact the prosthesis does not require a very high tensile or tear strength unless the patient stretch his/or her prosthesis and handle it harshly. The patient should follow the instruction for maintenance cautiously so that to extend the prosthesis service life.
Investigations of mechanical properties (tensile, tear, hardness and percentage elongation) of the printed samples were significantly different from control samples. In this study the results of the mechanical tests performed on tensile strength, tear strength, the percentage of elongation and hardness for the printed samples were found to be significantly different from the control samples. According to results obtained from this study, no one can suggest that the manufactured prosthesis does not last long or not better than the handmade prosthesis, because the ideal properties have not been standardized yet in terms of the mechanical properties.
Variation in mechanical properties of the test samples compared with controlled samples—pure silicone samples could be perhaps due to amount of starch in the test material, as starch provides a scaffold for the silicone polymer when it is used by Z-Corp printer to produce three dimensional 3D facial prostheses. The starch acts as filler for the 3D printed prostheses, a filler when added to the silicone polymer may increases hardness, and reduces tensile and tear strength, of course that is depend on the type and amount of the filler [35, 36]. Therefore, it was necessary to measure the weight or volume ratio of silicone polymers—the infiltrate to starch—the filler. Furthermore, and in order to understand the variation in the mechanical properties and the general drawback in these properties it was necessary to investigate depth of penetration of the infiltrate (silicone polymers) inside the printed starch models the quality of this infiltration. Therefore, Proper protocols were designed for
Percentage of starch by weight within fully infiltrated models
Depth of infiltration inside printed starch models
Quality of infiltration and degree of coherence between the starch particles and the silicone polymers.
For 3D printing soft tissue prostheses process, the 3D printed starch models is infiltrated by silicone polymers in order to provide skin texture and required elasticity and softness. The infiltration process affects overall quality of prostheses and therefore investigated below in different aspects.
As the starch powder implicated in fabrication of 3 dimensional soft tissue prostheses, it was necessary to determine the average amount of this powder within the total weight of prosthesis and their percentages by weight in the final prosthesis. in this investigation, 8 printed blocks of the starch powder (45 × 45 × 4 mm) were produced by Z510 printer. The blocks weighed using a sensitive digital balance (Mettler AJ100). Then the samples infiltrated with Sil-25 maxillofacial silicone polymers according to infiltration protocol mentioned in the previous section (3 bars for 25 minutes left for 25 hours) final setting time. Then the infiltrated blocks weighed again and percentage of each component within an infiltrated block was determined. Table 2 shows weight in gram, standard deviation and percentage of each component. The powder adds up to 40% of the total weight of the fully infiltrated blocks, whereas the silicone polymers comprising only 60%.
Weight in gram and SD | % By wight | ||
---|---|---|---|
Starch | Starch + Silicone pressure | Starch | Silicone pressure |
3.5 ± 0.04 | 8.50 ± 0.07 | 41.5% | 58.5% |
Percentage of silicone polymers and starch powder in fully infiltrated blocks.
As the printed starch models produced by the Z-Corp printer are solid and fragile, therefore, it was necessary to apply a specific protocol for infiltration of the silicone polymers into the printed models. For this purpose a set of 30 cubes measuring 20 × 20 × 20 mm were printed in starch, using Z-Corp (Z510) 3D printer, the starch cubes were infiltrated with Sil-25 maxillofacial silicone polymer under different conditions. One group served as control group, the cubes were infiltrated with Sil-25 maxillofacial silicone polymers, ratio (1–10) according to manufacturing standard. The cubes were then submerged in the polymer mixture and left under atmospheric air pressure at room temperature for a scheduled time, 5 minutes (
Bisected cubes stained to identify the depth of infiltration of silicone polymers, the dye is taken up by the hydrophilic starch, whereas the infiltrated area is hydrophobic and does not take up the dye.
Three other groups were served as test groups, testing infiltration depth was repeated on 30 cubes measuring 20 × 20 × 20 mm for each test group, but in this group the cubes were placed in a pressure vessel under 1, 2 and 3 bars pressure for a similar time schedule, 5 minutes (
Traveling microscope (Mitutoyo TM) with X-Y coordinate, used to measure the infiltration depth, 12 measurements on each sectioned cube.
Result of this study is shown in Figure 10 and Table 3, minimum depth of infiltration was detected under normal atmospheric pressure and room temperature, which was around 1 mm, this was slightly affected by length of time the cubes staid sank in the silicone polymers. Whereas, 2 and 3 bars pressure increased the infiltration depth of the silicone polymers significantly, which was also affected by length of time. Maximum infiltration depth was recorded for 3 bars pressure and at 25 minutes time. Results showed that pressure and time have significant effect on the depth of infiltration of the silicone polymers inside the powder cubes. Two ways ANOVA implied significant differences (
Infiltration of Sil-25 under normal air, 2 and 3 bars pressure and 5-time schedule.
Infiltration depth in (mm) and SD | ||||||
---|---|---|---|---|---|---|
Infiltration time (minutes) | 5 minutes | 10 minutes | 15 minutes | 20 minutes | 25 minutes | |
Silicone polymer | Pressure | |||||
Sil-25 | Air pressure | 0.94 (0.08) | 1.19 (0.01) | 1.16 (0.05) | 1.27 (0.13) | 1.35 (0.08) |
Sil-25 | 2 Bar | 1.99 (0.10) | 2.76 (0.23) | 3.30 (0.28) | 3.75 (0.19) | 3.88 (0.17) |
Sil-25 | 3 Bar | 3.94 (0.15) | 5.43 (0.20) | 6.36 (0.51) | 7.71 (0.27) | 8.65 (0.49) |
Infiltration depth of Sil-25 inside 3D printed starch blocks under different pressure and at different time schedule.
According to result obtained from this study, it can be concluded that infiltration depth of Sil-25 silicone polymers is significantly influenced by pressure applied. Under 3 bars and 25 minutes time, the infiltration depth recorded more than 8 mm from all sides, this would suggest that infiltration depth inside a prosthesis would be around 16 mm and reasonably this depth will be sufficient for soft tissue facial prostheses.
Evaluation of the infiltration quality of silicone polymers inside the 3D printed starch powder was required to characterize the interaction between the hydrophobic silicone polymers and the hydrophilic starch powder. It is acknowledged that the mechanical and optical properties of the 3D printed prostheses depend basically on material properties and characterization, which consequently determine the service life of the prostheses and determine its ability to resist the environmental factors such as UV from sunlight humidity, body secretion and weathering temperature. The previous section determined depth of infiltration of the infiltrate inside the printing powder, 8 mm penetration depth was achieved. However, we did not realize how consistent/homogeneous this infiltration was. Therefore, SEM was carried out to characterize an important aspect of 3D color printing facial prostheses and to detect any flaw in the structure of the composite that is utilized in fabrication of facial prostheses.
Scanning Electron Microscopy SEM was applied for this purpose to prepare and obtain various samples of printed starch blocks infiltrated with two different maxillofacial silicone polymers (Sil25 and Promax 10) in order to examine the quality of the infiltration inside the starch printed blocks. SEM pictures of the printed blocks were compared with hand mixed of 40% starch powder and 60% Sil25 silicone polymers. Hand mixed blocks were prepared by mixing the starch and the silicone polymers for 1 minute to obtain a homogenous mixture, then the mixture poured into a 75 × 75 × 4 mm stainless steel mold, pressed and left for 24 hours in ambient temperature. Then slices from the three blocks were prepared using surgical blade number 11 and send for SEM to be examined with SEM of starch powder alone.
SEM analysis of the starch powder, 3D printed blocks infiltrated Sil-25 and Promax10 plus the hand mixed blocks are shown in Figure 11, the SEM of the powder and of the infiltrated powder blocks showed amorphous, non-crystalline shaped particles with different particle sizes varies from very small to relatively large particles. These particles appeared to be loosely arranged and randomly orientated with some spaces in between these particles and disorganized spreading of the starch powder within the silicone polymers leaving big gaps between the powder particles. Incorporation of starch powder with Sil-25 maxillofacial silicone and Promax10 under 3 bar pressure are seen in Figure 11A and B, showing almost similar distribution of the powder within the infiltrates. However, better incorporation and more homogenous distribution of starch particles within the silicone polymers in hand mixed of 40% powder incorporated into 60% infiltrate of silicone polymers by weight (Figure 11C). This could be attributed to the layer of binder on the outer surfaces of the printed blocks that might an obstacle for the infiltration process.
SEM for (A) 3D printed starch block infiltrated Sil-25 SP (×180), (B) 3D printed starch block infiltrated Promax10 (×189), (C) hand mixed starch powder and Sil-25 SP (×178), (D) starch powder particles (×341).
Figure 12 is a magnified SEM image (×707) of hand mixed starch powder and Sil-25 silicone polymers. Although at a lower magnification the sample apparently seems to be very properly infiltrated having smooth texture, however, under higher magnification the composite shows evidence of porosity and spaces between the powder particles and the silicone polymers within the composite. This phenomenon indicates lack of coherence and integrity between the hydrophobic silicone polymers and the hydrophilic starch powder, which, is related to the wettability and viscosity between silicone polymers that have low surface energy and strongly hydrophobic [37] and starch powder is hydrophilic in nature [38].
SEM for Sil-25 hand mixed samples showing spaces around the starch particles (×707).
Furthermore SEM sections (Figures 11C and 12) showing gaps and voids, which indicate tripping of air especially in central parts of the blocks under infiltration pressure. Lack of interaction and incorporation between the starch powder particles and the silicone polymers that are utilized by Z-Corp printer and employed for fabrication of soft tissue facial prostheses will influence the general properties and material’s integrity, which my finally affect the durability of the prostheses. Therefore, it was necessary to test the mechanical properties of the 3D printed samples that are going to be used for fabrication of soft tissue facial prostheses.
Skin color is vital for quality of facial prostheses. Previous research has focused on reproducing skin color and assess their color appearance difference under standard lighting conditions [39, 40]. The advent of new lighting technologies such as Halogen and LEDs generates new challenges for rendering skin on displays, in print, but most importantly, for synthetically generated skin prostheses, since ambient illumination can change the appearance of both natural and synthetic skin, but not necessarily in the same way [41]. Here skin appearance models not only need to take into account different ambient illuminations, but also the three-dimensionally geometry of the human face and differences in the methods for reconstruction—surgery, prosthetics or medical make-up/tattooing [42]. Therefore, to truly reproduce appearance of skin color under different illumination and objectively evaluate their color quality, follow steps are develop:
Step 1: Measurement of skin spectral reflectance of subject
The measurement of skin spectral reflectance would be affected by these various parameters, including the measurement instruments, measurement distance, measurement location, the instrument aperture size, the pressure applied to the skin by the instrument, as well as the gender and ethnic group [43]. Spectrophotometer is recommended for facial prostheses application, since it is independent of lighting applied and highly consistency [44].
Step 2: Develop spectral color profile for 3D camera
3D camera can be used to capture facial and body image. A spectral reflectance estimation need to conduct to transform camera RGB to spectral reflectance for each pixel of 3D image [45]. Spectral color database [46, 47]need to be used as training sample to obtain base function for spectral reflectance estimation.
Step 3: Develop spectral color profile for 3D printer
For 3D color printing, spectral color profile also needs to develop to transform spectral reflectance of human skin in each pixel of 3D image to printer CMYK value for color printing. Post printing processing also needs to conducted for infiltration process as described in previous section
Step 4: Color quality evaluation
To evaluate color quality of facial prostheses, the average CIELAB color difference (ΔEab) under several standard CIE illuminants needs to calculated. To test spectral reproduction, the root-mean-square error (RMSE) and goodness-of-fit coefficient (GFC) needs to apply [48].
Drawback in the mechanical properties of the printed samples mostly attributed to the amount of starch (40%) and due to lack of coherence and integrity between the hydrophobic silicone polymers and the hydrophilic starch powder that form the scaffold for the test samples as Z Corp 3D printer utilizing starch powder for printing which led to draw back in the mechanical properties of the final product. Perhaps the prostheses will have a shorter service life than the conventional pure silicone prosthesis. However, printing several prostheses at time of printing could compensate the drawback in the mechanical properties. The technology applied enabled construction of several copies of the prostheses in a shorter time frame and at a lower cost than handmade silicone polymer prostheses. Another advantage of applying rapid prototyping is that producing the required thickness of the missing part that rendering a lightweight prosthesis, which is mostly valued by the patients (Figure 13).
3D printed nasal prosthesis showing nostril opened due to controlled thickness of the prosthesis.
Furthermore, designing a prosthesis by using 3D software package can also allow the anaplastologist to save the design and all patients data to utilize it for printing future copies of the prosthesis on the patients’ demand and with only light modification in the design of the prosthesis if there is any tissue change at the site of the defect [28]. Finally we believe that the many limitations of handmade prostheses regarding esthetics, high prosthesis cost, time, effort, hectic impression techniques and problems of retention plus high technical skill required for fabrication by anaplastologist could be generally reduced and consequently minimizing the social and psychological challenges that often-maxillofacial patients encountered in life.
At this stage, a fully computerized customized prosthesis is manufactured, using biocompatible materials [49]. The prosthesis matching the patient’s skin color and having skin-like texture with accurate anatomical details of the patient, possessing a light weight with controlled thickness of the prosthesis that is well appreciated by the patients as shown in (Figure 14).
Nasal prostheses produced by Z510-3D color printer.
Despite the many advantages of this technology in constructing soft tissue facial prostheses, there were few limitations compared to handmade—conventional method of fabrication. These limitations were related to the mechanical properties of the final product [50]. The mechanical tests shows drawback in the mechanical properties, however, it is hard to judge how poorly that will affect the prosthesis on the patient; the only real way of testing mechanical and optical durability is when the prostheses test on the patients during the service life of the prosthesis. As the project was at the experimental stage of development it wasn’t possible to perform these tests on patients [28]. More work should be done to determine how long the prostheses would last. So far it is obvious that the prostheses done need to be replaced regularly. Further investigations should be done on the printing materials in order to improve the mechanical properties and durability of the prostheses and to achieve optimal advantages of time compression technology and rapid prototyping for simple, full automated fabrication of facial prostheses.
Color matched maxillofacial prosthesis was fabricated using Z-Corp 510 color printer utilizing starch based biocompatible materials. According to the mechanical properties, the prosthesis should be replaced in a range of 6–12 months. The prosthesis could be used as interim prosthesis special after surgery while the patient is going through healing period. Furthermore the prosthesis could be used as definitive prostheses by compensating the draw back in the mechanical properties by taking the great advantages of this great technology that having the ability of printing several copies of the prosthesis at the time of printing at lower cost and rapid manufacturing of anatomically more accurate parts compared to handmade prostheses and applying more comfortable methods of data capturing, designing and manufacturing.
Reactive distillation is an operation that incorporates chemical reaction and physical separation in a single unit [1]. This process, when applicable, has several potential advantages for the industry when compared to conventional systems, such as the reduction of capital costs, improvement of component separations, use of fewer instruments for monitoring, reduction of energy costs, and improvement in reaction selectivity [2, 3, 4, 5, 6]. Among the possible applications of reactive distillation, the separation of azeotropic mixtures and compounds with similar boiling points can be carried out by adding a reagent that promotes the consumption of one component of the mixture and forms a product with markedly different physical properties, thereby favoring separation. However, the use of reactive distillation is more frequent in systems where product formation is limited by chemical equilibrium. This separation technique allows for the constant removal of one or more products promotes or increases reactant conversion [7].
The first patents for the reactive distillation process were published in the 1920s, developed by Backhaus for the production of esters [8, 9, 10]. However, few industrial applications were developed before the 1980s [11], when the commercial process for methyl acetate synthesis via reactive distillation with a homogeneous catalyst was patented by Agreda and Partin [12], in collaboration with the Eastman Chemical Company. This application of reactive distillation is considered an exemplary case because of the substantial reduction in process costs (∼80%) [13] achieved through the elimination of units, such as reactors and separation columns, and the possibility of heat integration. The conventional methyl acetate synthesis process (Figure 1), which comprises 11 different steps and 28 pieces of equipment, was replaced by only a highly integrated reactive distillation column (Figure 2), enabling the aforementioned reduction in process costs. Recent uses of reactive distillation in chemicals production such as acetic acid and methanol [14], alkyl carbonates [15], butadiene [16], butyl acetate [17], carboxylic acids and ether [18] and carboxylic esters [19] are described in patents.
Schematic representation of the conventional process for the synthesis of methyl acetate. Caption: R01: reactor; S01: mixer; S02: extractive distillation; S03: solvent recovery; S04: methanol recovery (MeOH); S05: extractor; S06: azeotropic column; S07-S09: flash columns; S08: acetic acid recovery; V01: decanter.
Schematic representation of the integrated process for the production of methyl acetate by reactive distillation.
Reactive separation conveniently combines the production and removal of one or more products, enabling improvements in reaction productivity and selectivity. However, despite the benefits of this process, the planning and control of reactive distillation columns are hindered by complex interactions between reaction and separation. Operation conditions resulting in the formation of equilibrium between liquid–liquid–vapor phases, low mass transfer rates between liquid and vapor phases or diffusion inside the catalyst (for heterogeneous reactions) and chemical kinetics with reduced reaction rate need to be avoided or minimized [20]. Additionally, since both separation and reaction take place simultaneously in the same unit, the temperatures and pressures required for the two steps must have similar values [21]. If the overlap of operating conditions is not significant, the use of reactive distillation is not recommended [2].
The trend toward the use of biofuels has resulted from increased attention to topics related to mitigating environmental impacts by reducing the consumption of fossil fuels [22]. In this context, biodiesel is considered the main substitute fuel for diesel oil due to its lower polluting potential and the possibility of being used in diesel engines without the need for significant modifications [23, 24].
Biodiesel (alkyl esters) can be obtained by several reaction routes. The most conventional of them is the transesterification of triglycerides (oils) with short-chain alcohols (Eqs. (1)–(3)), such as methanol and ethanol, with homogeneous alkaline catalysts [25]. However, the raw material needed for this reaction must have reduced levels of acidity and moisture to avoid saponification reactions [26]. Due to the high costs of obtaining feedstocks that meet these specifications and competition with the food industry, studies aimed at the production of biodiesel from residual oils with a high content of fatty acids have been published [27, 28, 29, 30].
One of the alternatives to reduce the typical acidity of residual oils is to carry out a previous fatty acid esterification step [25] (Eq. (4)). However, in this process, with reaction and separation occurring in different units, chemical equilibrium limits the yield because the reaction that forms one of the products, water is reversible [31].
Therefore, the use of a reactive distillation column can be justified and may result in higher fatty acid conversions compared to those achieved in conventional systems. Tables 1 and 2 provide an overview of the literature on esterification and transesterification reactions aimed at producing biodiesel.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | FFA conv. |
---|---|---|---|---|---|
[31]a | Oleic acid | H2SO4 (1–3 FFA wt%) | 130–150°C | — | 79.6% |
[32]a | Dodecanoic acid | Solid acid catalyst (0–5 FFA wt%) | 120–180°C | AspenOne 2004 | ∼95.0% |
[33]a | Dodecanoic acid | Metal oxides (0–10 FFA wt%) | 120–180°C | AspenOne 2004 | ∼72.0% |
[34]a | Non-edible oil mixture | Tin (II) chloride (1–9 oil wt%) | 40–60°C | Aspen Plus V8.8 | 78.3% |
[35]b | Dodecanoic acid | Sulfated Zirconia | 130°C (FFA feed) | Aspen Plus V9 | 99.9% |
[36]a | Fatty acids mixture | Nb2O5 (5 FFA wt%) | 90–170°C | Aspen Plus V7.3 | 96.0% |
[37]b | Fatty acids mixture | Sulfuric acid | 417°C (FFA feed) | Aspen Plus | — |
[38]b | Fatty acids | Solid acid catalysts | 58, 145, 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[39]b | Hydrolyzed soybean oil | Niobium oxide | 207°C (FFA feed) | Fortran Algorithm | >98.0% |
[40]a | Fatty acids | Sulfuric acid (0.33–0.66 wt%) | 50–70°C | Aspen Plus V10 | ∼90.0% |
Reactive distillation studies aimed at the production of biodiesel through esterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
Ref. | Feedstock | Catalyst | Temperature | Simulation software | Yield |
---|---|---|---|---|---|
[41]a | Soybean oil | NaOH (0.5–1.5 oil wt%) | 50°C (oil feed) | — | 98.2% |
[42]a | Canola oil | KOH, KOCH3 (0.73–1.83 oil wt%) | 100–160°C (reboiler) | — | 94.9% |
[43]a | Canola oil | KOH | 95–150°C (reboiler) | — | 94.4% |
[44]a | Cooking oil | 12-Tungstophosphoric acid hydrate | 20–30°C (oil feed) | — | 93.8% |
[45]a | Palm oil | KOH (0.5–2 oil wt%) | 85–120°C (reboiler) | — | 92.3% |
[46]b | Triolein | NaOH | 55°C (oil feed) | CHEMCAD/MATLAB | 90.3% |
[47]b | Soybean oil | NaOH | 25–90°C (oil feed) | HYSYS | ∼97.0% |
[48]b | Algal oil | H2SO4 | <150°C (reboiler) | MATLAB | — |
[49]b | Trilinolein | NaOH/Magnesium methoxide | 60–150°C (column) | Aspen Plus | 98.3% |
[50]b | Soybean oil | NaOH/CaO + Al2O3 | 60°C/25°C | Aspen Plus V8.4 | — |
Reactive distillation studies aimed at the production of biodiesel through transesterification.
Experimental tests performed by the authors.
No experimental tests performed by the authors.
In general, the main objective of the evaluated studies on esterification in reactive distillation columns is to increase the yield of the biodiesel production process by shifting the chemical equilibrium of the reaction. However, for transesterification studies, reactive distillation systems are considered mainly due to the possible reduction in the reaction time or in the purification costs of the biodiesel alkyl esters formed. This difference in purpose presumably originates from the necessity of milder transesterification reaction conditions when compared to the esterification route for the attainment of high biodiesel yields.
Mathematical modeling of a reactive distillation column was developed in [51] with considerations described in [52]. In their study, the authors assume the absence of chemical equilibrium in the stages and steady-state operation, with reaction rates being explicitly considered in the model of each stage, Murphree separation efficiency equal to 100% and feeding performed by single-phase streams.
Such methodology, presented in this section, was used in the studies of the references [2, 38, 39,53]. The nomenclature for the terms used in the equations is described in Table 3.
Symbol | Description |
---|---|
Ci,j | Molar concentration |
CLp,i | Molar specific heat in the liquid phase |
Ej | Relationship between vapor and liquid streams |
Fi,j | Feed molar flow |
f eqi,j | Phase equilibrium function |
f mi,j | Mass balance function |
f vlj | Function that correlates liquid and vapor streams |
f rk,j | Reaction rate function |
H Ij | Total enthalpy of the vapor stream |
HjII | Total enthalpy of the liquid stream |
k k,j | Kinetic reaction constant |
nIi,j | Molar flow of vapor |
nIIi,j | Molar flow of liquid |
nc | Number of components |
nr | Number of chemical reactions |
Psati,j | Liquid saturation pressure |
Pj | Pressure |
Qj | Heat added or removed |
R | Universal gas constant |
Rj | Liquid withdrawal fraction |
Tj | Temperature |
Tref | Reference temperature (298.15 K) |
vIIj | Liquid molar volume of pure compound |
xIi,j | Molar fraction in vapor phase |
xIIi,j | Molar fraction in liquid phase |
Zj | Lateral vapor withdrawal fraction |
αi,k | Kinetic order of reaction |
Δhvapi | Molar enthalpy of vaporization |
γvapi,j | Activity coefficient in the liquid phase |
νi,k | Stoichiometric coefficient |
ξ k,j | Reaction rate |
Nomenclature of terms used in mathematical modeling.
(references: i = component, j = column stage, k = reaction).
The generic plate scheme adopted by the authors is represented in Figure 3.
Configuration of each stage j in the reactive distillation column. Source: [
Eq. (5), which represents the mass balance of component i in stage j of the column as a residual function, is given by:
Assuming that the streams that leave the stage are in phase equilibrium, Eq. (6) relates the mole fractions in the liquid and vapor phases:
In this expression, the Poynting correction and the fugacity coefficient of the pure saturated compounds are neglected. In addition, and the vapor phase is considered to be an ideal gas mixture as a consequence of the assumption that the column operates at low pressure, close to atmospheric conditions.
The equation for the rate of reaction k in stage j is represented by Eq. (7), which can be expressed as a residual equation by applying the logarithm function (Eq. (8)).
Assuming that the molar volume of the liquid phase is that of an ideal solution and describing Eq. (8) as a function of the activity coefficients of the components in the liquid phase, Eq. (9) is obtained.
Eq. (10), which describes the energy balance of stage j, is needed to calculate the temperature, which is different at each stage of the reactive distillation column. Positive and negative values of Qj correspond to the heat being supplied to or removed from the column, respectively.
The ratio between the molar flows of vapor and liquid leaving each stage of the column is represented using Eq. (11). This equation is intended to make the condenser and reboiler specifications more flexible by associating the relationship between the liquid and vapor streams that leave the column stages.
When written in the residual form, as in Eq. (12), the equation has the following form:
The values of the Ej parameter for each form of operation of the condenser and reboiler (partial or total) are shown in Table 4.
Reboiler (stage 1) | Condenser (stage N) | ||
---|---|---|---|
Partial | Total | Partial | Total |
Z1 = 0 | Z1 ≠ 0 | ZN = 0 | ZN = 0 |
R1 = 0 | R1 = 0 | RN = 0 | RN ≠ 0 |
E1 ≠ 0 | E1 → ∞ | EN ≠ 0 | EN = 0 |
Characteristics of column heaters (reboiler and condenser).
In the study developed by [2, 36, 37, 48], all cases of simulated reactive distillation column configurations used a partial reboiler and total condenser (E1 ≠ 0 and EN = 0).
Solving the set of equations that describe a reactive distillation column is an arduous task, and rigorous mathematical models aimed at a computer simulation of this type of equipment were not developed until the 1970s [54].
In recent decades, commercial software that has specific models and algorithms for reactive distillation operations has been widely used, as shown previously in Tables 1 and 2. The simulations developed in the subsequent section, referring to case studies applied to biodiesel production and co-product valuation, use the RADFRAC module present in the commercial Aspen Plus software, which solves the equations of mass balance, energy balance, phase equilibrium and the sum of molar fractions (MESH) [55] through the “inside-out” algorithm [54].
The kinetic parameters for the esterification of fatty acids (FFA) present in corn distillers oil from DDGS (dried distillers grains with solubles) were estimated by a model fitting of the FFA conversion data (Table 5) obtained by our group. The reaction (Eq. (13)) was carried out at the temperatures of 150, 175 and 200°C, with ethanol and NbOPO4 (catalyst), following a molar alcohol:FFA ratio of 10:1 and catalyst load of 10% (FFA mass).
Temperature (°C) | Time (min) | FFA conversion (%) |
---|---|---|
150 | 15 | 1.68 |
30 | 3.99 | |
60 | 5.25 | |
120 | 9.68 | |
180 | 12.23 | |
240 | 19.44 | |
360 | 28.39 | |
175 | 15 | 10.13 |
30 | 15.36 | |
60 | 19.36 | |
120 | 24.21 | |
180 | 34.74 | |
240 | 43.06 | |
360 | 37.88 | |
200 | 15 | 9.46 |
30 | 17.97 | |
60 | 29.41 | |
120 | 38.35 | |
180 | 45.89 | |
240 | 48.53 | |
360 | 49.55 |
FFA conversion of the esterification reaction kinetic tests.
The methodology applied aims to estimate the pre-exponential factor (k0) and the activation energy (Ea) of the reaction. To fit the kinetic parameters, the objective function to be minimized is the squared difference between the experimental values of the FFA conversion and those calculated with a reversible pseudo-homogeneous model (Eq. (14)). The reaction rate (rF) equation was applied to Eq. (15), which describes a batch reactor in terms of the FFA conversion (x) in a given time (t).
A Nelder–Mead [56] simplex algorithm and a 4th order Runge–Kutta [57] method were used to perform the objective function minimization and conversion equation integration steps, respectively. The reaction rate (rF) was obtained through the model and the specific reaction rate constants k (L/mol.s) were expressed by the Arrhenius equation (Eq. (16)). Through this methodology, the parameters Ea and k0 for the reaction rate constants of the forward and reverse esterification reactions were estimated.
In these equations,
T = Absolute temperature at which the kinetic test was performed (K).
R = Universal gas constant (J/K.mol).
F = Fatty Acids (FFA).
A = Alcohol (ethanol).
E = Ethyl Esters (FAEE).
W = Water.
Cn = Concentration of compound n (mol/L).
t = time (s).
The compounds defined for the simulation of the fatty acid esterification with ethanol were specified in the Aspen Plus V.12 process simulator, with the fatty acid and oil fraction represented by oleic acid and triolein, respectively. A similar approach was used by other researchers as a simplification of the numerous components of the acid and oil fraction of the feedstock [58, 59, 60]. The NRTL thermodynamic model [61] was selected to evaluate the activity coefficients of the components of the reaction mixture and the NRTL binary interaction parameters missing from the simulator database were estimated directly through the Aspen Plus estimation tool that uses the UNIFAC model [62].
The flowsheet developed for the process simulation is shown in Figure 4 and consists of two columns, the first being responsible for the reactive distillation of the reactants fed to the process (C-EST) and the second for removing approximately 95% of the ethyl esters (FAEE) produced (C-DIST).
Flowsheet of the FFA esterification process (F = feed, P = product, S = intermediate stream, H = heat exchanger, B = pump, V = valve, C = column).
The simulated reactive distillation column has 22 total stages, of which 14 compose the reactive zone (5 to 18), while the C-DIST column consists of 10 total stages. The columns operating parameters are presented in Tables 6 and 7. Both the distillation columns have kettle-type reboilers, however the C-EST column is equipped with a total condenser, while the C-DIST with a partial condenser to separate the ethyl esters from the remaining oil and excess ethanol. It is noteworthy that the liquid phase composition and temperature profile graphs, as well as the conversions obtained follow the data referring to the process after the optimization described later.
Parameters | Before optimization |
---|---|
Stages | 22 |
Oil feed stage | 5 |
Ethanol feed stage | 18 |
Absolute pressure (bar) | 4 |
Distillate: feed molar ratio | 0.62 |
Reflux molar ratio | 0.08 |
Reactive distillation column operating parameters (C-EST).
Parameters | Distillation column |
---|---|
Stages | 10 |
Feed stage | 4 |
Absolute pressure (bar) | 0.03 |
Distillate: feed molar ratio | 0.51 |
Reflux molar ratio | 1.5 |
Distillation column operating parameters (C-DIST).
The H-1 and H-2 heat exchangers are responsible for heating the oil (F-OIL) and ethanol (F-ETOH) streams up to 200°C and 50°C, respectively, shown in Table 8, while the pumps B-1 and B-2 increase the pressure of the feed streams from 1 bar to 10 bar.
Stream | F-OIL | F-ETOH |
---|---|---|
Temperature (°C) | 20.00 | 20.00 |
Absolute pressure (bar) | 1.00 | 1.00 |
Enthalpy (kW) | −4058.33 | −2461.46 |
Mass Flow (kg/h) | ||
Oleic acid (FFA) | 900 | — |
Ethanol | — | 1467.86 |
Triolein | 5100 | — |
Ethyl oleate (FAEE) | — | — |
Water | — | — |
Properties of the oil feed and ethanol streams (F-OIL and F-ETOH).
The optimization of the process parameters of the esterification reactive distillation column was performed using the MATLAB® R2020b software by implementing the MEIGO package (Metaheuristics for Bioinformatics Global Optimization) [63], an optimization supplement for global optimal search which can be used to optimize industrial processes [64, 65]. The results obtained through Aspen Plus simulations were provided to MATLAB, where the optimization algorithm was performed, and new obtained values of the variables evaluated were used to carry out new simulations iteratively.
The Particle Swarm Optimization (PSO) method was applied to minimize the objective function that describes the conversion of fatty acids (Eq. (17)), starting from an initial population of 50 particles (solution vectors) defined by the algorithm in the pre-defined search intervals.
For the simulation, the varied parameters were molar reflux ratio, internal pressure, molar ratio between distillate stream and total feed, and oil and ethanol feed stages. As restrictions, the reboiler temperature, the recovery of the desired product (ethyl esters) at the bottom of the column and the feed stages of the reagents were evaluated with Eqs. (18)–(20). The reboiler temperature upper limit was defined as 200°C to avoid degradation of the reagents or products and excessive use of the hot utility. It is observed that the minimization of the negative value of the conversion corresponds to the maximization of its positive value.
In these equations,
The kinetic constants obtained through the discussed methodology are presented in Table 9, with the direct reaction of ethyl esters formation indicated by the subscript “1” and the reverse reaction of fatty acids formation indicated by the subscript “2”. Figure 5 shows the comparison between the experimental and calculated conversions, along with the R2 coefficient of the fit for each temperature.
Parameter | Value |
---|---|
K0,1(L/mol.s) | 252.786 |
K0,2(L/mol.s) | 207.093 |
Ea1 (J/mol) | 51,357.1 |
Ea2 (J/mol) | 39,244.1 |
Estimated kinetic constants for the esterification reaction.
Experimental (−) and calculated FFA conversion at 150°C (∆), 175°C (
Observing the results presented, it is noted that the data fitting at 200°C presented a high coefficient of determination, while the data fitting at 175°C obtained a reduced R2. However, as the temperature in the reactive section of the esterification column is, on average, close to 195°C, it was concluded that due to the excellent results achieved in the data fitting at 200°C, the use of the estimated kinetic parameters would not hinder the development of a simulation faithful to the real behavior of the reaction.
The composition and temperature profiles along the stages of the reactive distillation column (column C-EST in Figure 4) are presented in Figures 6 and 7.
Liquid phase mass composition profile (C-EST).
Column temperature profile (C-EST).
The liquid phase composition profile of the C-EST column (Figure 6) indicates that the major component for all stages with values higher than 5 (closer to the bottom) is triolein, while in the others there is a predominance of oleic acid, ethyl oleate (FAEE), ethanol and water, since only negligible amounts of triolein are evaporated along the column, as seen in the vapor phase mass composition profile. Additionally, there is a significant increase in the fraction of ethanol and water in the liquid state in the first stage due to the use of a total condenser in the reactive distillation column.
The composition of the streams that characterize the main products of the process (S-FAEE, P-OIL and P-FAEE) are presented in Table 10 and, based on the simulation results, there is a final fatty acids conversion (mol) of 83.97% inside the column, 94.00% of which is recovered in the P-FAEE stream, while 5.83% is recovered in the P-OIL stream. The remaining 0.17% of FAEE is located at the P-ETOH2 stream. The resulting stream of the desired product (P-FAEE) has a purity (FAEE) greater than 98%, resulting in an ester content superior to the value described in Brazilian and European specifications [66, 67].
Stream | S-FAEE | P-FAEE | P-OIL |
---|---|---|---|
Temperature (°C) | 162.39 | 90.00 | 305.08 |
Absolute pressure (bar) | 8.13 | 0.03 | 0.03 |
Enthalpy (kW) | −3813.85 | −506.62 | −2532.30 |
Mass Flow (kg/h) | |||
Oleic acid (FFA) | 144.22 | 11.80 | 132.41 |
Ethanol | 186.96 | 0.58 | — |
Triolein | 5100.00 | — | 5100.00 |
Ethyl oleate (FAEE) | 830.34 | 780.97 | 48.43 |
Water | 0.58 | — | — |
Composition of the main product streams.
In Table 10, it is possible to observe that there are still traces of ethyl esters present in the oil stream. However, this amount corresponds to less than 1% of the total mass fraction of the stream. P-OIL, therefore, was considered to be non-significant. Furthermore, of the 900 kg/h of FFA fed to the process, only 132.41 kg/h remain, characterizing a reduction of 85.29% of the total fatty acid mass. Finally, the energy demands for H-1, H-2, condensers and reboilers of columns C-EST and C-DIST are presented in Table 11.
Equipment | Energy demand (kW) |
---|---|
H-1 | 619.71 |
H-2 | 31.60 |
C-EST (condenser) | −295.57 |
C-EST (reboiler) | 330.86 |
C-DIST (condenser) | −581.94 |
C-DIST (reboiler) | 1095.36 |
Energy demand of the process equipment.
Table 12 shows the limits and initial estimates for the variables evaluated for the optimization of the esterification process. Table 13 displays the constraints imposed on the reboiler temperature, ethyl ester recovery fraction (FAEE), and conversion. The values chosen as initial estimates were obtained by manually setting different values for the reflux molar ratio, condenser pressure, and distillate feed molar ratio, and adopting the best result obtained.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Lower Limit | 0.005 | 0.01 | 0.50 | 5 | 5 |
Upper Limit | 2 | 10 | 0.70 | 18 | 18 |
Initial Estimate | 0.08 | 4 | 0.62 | 5 | 18 |
Lower, upper limits and initial estimates for the variables evaluated in the esterification reaction optimization process.
Restrictions | Reboiler temperature (°C) | Recovery fraction of FAEE | Conversion (%) |
---|---|---|---|
Lower Limit | −273.15 | 0.99 | 0 |
Upper Limit | 200 | 1.00 | 100 |
Initial constraints for the response variables for the variables evaluated in the esterification reaction optimization process.
The results obtained are shown in Figure 8, with a maximum conversion of 83.97% and the final values of the variables are added to Table 14.
Evolution of the FFA conversion as a function of the number of optimization iterations.
Variable | Molar reflux ratio | Condenser pressure (bar) | Distilled molar ratio: feed | Oil feed stage | Ethanol feed stage |
---|---|---|---|---|---|
Result | 0.1130 | 8.1314 | 0.6806 | 5 | 18 |
Response vector of input variables for the esterification reaction optimization process.
An additional simulation performed in a CSTR reactor achieved an FFA conversion of 51.06%, while the maximum average conversion in the kinetic tests (200°C) was 49.55%. The simulated CSTR operated at a constant temperature of 200°C with the residence time of 3 h (same duration of the experimental tests) and was fed with streams following equal mass flows and compositions to the RDC column feed streams. Thus, the optimization results represent a significant improvement of 64.45% and 69.46% compared to the CSTR and experimental tests, respectively, inferring that the use of a reactive distillation column could be beneficial to the process.
As biodiesel production increases so do the production of glycerol as for each liter of biodiesel produced, approximately 100 mL of crude glycerol are obtained [68]. Among the transformation processes for glycerol to viable chemical intermediates, glycerol ketalization for the production of solketal has gained prominence. Solketal can be used as an additive to increase the octane and fluid dynamic properties of the fuel. The addition of up to 5% by volume of solketal to gasoline leads to a significant decrease in gum formation [69]. With this motivation, this study aims to simulate the operation of a reactive distillation column for the production of solketal from glycerol with acetone using heterogeneous catalysis, with high conversion of reagents and separation of the components of the reaction.
The applied methodology considers the ketalization reaction of glycerol (G) with acetone (A), forming solketal (S) and water (W). The reaction is considered reversible and elementary, being described by Eq. (21):
A pseudo-homogeneous model was used to describe the reaction kinetics through a system of differential equations of concentration over time, at different temperatures, in which the kinetic constants of the direct and inverse reaction are represented, respectively, by k1 and k−1(L/mol.s), while the molar concentrations (mol/L) of the species involved are given by CG, CA, CS and CW (Eq. (22)).
The solution of the system of differential equations using a 4th order Runge Kutta method [57] and the fitting of the kinetic parameters, k1 and k−1, and subsequent estimation of the Arrhenius equation parameters were performed by a Nelder–Mead simplex algorithm [56]. The experimental data used was retrieved from the study of [70].
The kinetic parameters evaluated were later used to predict the solketal formation reaction in a reactive distillation column, using the rigorous RADFRAC distillation model of the Aspen Plus commercial simulation software. The system considered in this study is shown in Figure 9.
Flowsheet of the solketal production process used in this study.
Using the estimated kinetic parameters, the glycerol ketalization reaction for the production of solketal was modeled in the Aspen Plus software. For the process simulation, the pressure inside the column was set at 10 atm. The feeding of the 13-stage column, RDC in Figure 9, are streams GLI-02 and ACE-02, originated from the heating of the currents GLI-01 and ACE-01 up to 95°C and 55°C, by the heat exchangers H1 and H2, respectively.
The ACE-01 stream is composed only of acetone, while GLI-01 contains 80% glycerol and 20% water by mass, disregarding other components such as methanol or dissolved salts normally present in glycerol from biodiesel production processes [71]. The products of reactive distillation are characterized by TOP-P and BOT-P streams, which correspond, respectively, to the streams rich in the most volatile and least volatile substances in the process.
Figure 10 shows the concentrations as a function of time according to the fitted kinetic parameters data.
Experimental and calculated concentrations (80°C).
Analyzing Figure 10, it is observed that the curves generated using the fitted parameters represented the experimental data satisfactorily. Table 15 presents the process specifications obtained after a sensitivity analysis, aiming to simulate a column with optimal operating conditions. Figure 11 shows the composition profile in the liquid phase as a function of the column stage number (1 = condenser and 13 = reboiler).
Parameter | Description |
---|---|
Number of stages | 13 |
Condenser type | Total |
Reboiler type | Kettle |
Molar reflux ratio | 0.69 |
Reboiler/condenser heat duty | 55.000 / -43.723 cal/s |
Column pressure | 10 bar |
Glycerol feed | 3rd stage |
Acetone feed | 11th stage |
Feed properties | 95 and 55°C - 1 bar |
Glycerol feed molar flow | 2.500 kmol/h |
Water feed molar flow | 0.625 kmol/h |
Acetone feed molar flow | 15.000 kmol/h |
Ketalization reaction stages | 3 to 11 |
RDC column specifications.
RDC column liquid phase composition profile.
The conversion of glycerol obtained for the operational conditions defined for the simulation was 98.2%, indicating the reaction occurred inside the column.
The SOLKETAL stream in Figure 9 has 99.53% solketal and the WATER stream consists of 99.82% water, on a mass basis. Thus, the simulations show that the methodology employed results in a high purity solketal product stream with solketal conversion superior to 98%. However, additional studies are needed to assess the effect of possible intermediate reactions on the process yield.
In this chapter, a general introduction regarding reactive distillation technology and its application to the biodiesel production process was presented. A literature-based mathematical model to describe reactive distillation columns was discussed, along with experimental and simulation studies developed by the authors of this chapter, using commercial software such as Aspen Plus.
In the case study of biodiesel production through the esterification of a low-cost feedstock, the application of an optimized reactive distillation column promoted an improvement of approximately 70% about FFA conversion. The resulting product stream attained purity above 98% in relation to alkyl esters. Additionally, the production of solketal aiming at the valorization of a co-product of the biodiesel production process (glycerol), was studied through the development of a flowsheet in the Aspen Plus simulator, resulting in a solketal stream with purity above 99%.
The results obtained through the developed studies indicate that the reactive distillation technology, applied to fatty acid esterification reactions for the production of biodiesel and ketalization of glycerol for the production of solketal, is promising and attractive in technical terms, however, further studies are necessary to analyze the economic feasibility of both processes.
The authors thank UTFPR and Sinochem Petroleum Brazil Limited (project 001/2019) for financial support.
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This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. 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They are known as hydrous phyllosilicate having silica, alumina and water with variable amount of inorganic ions like Mg2+, Na+, Ca2+ which are found either in interlayer space or on the planetary surface. Clay minerals are described by presence of two-dimensional sheets, tetrahedral (SiO4) and octahedral (Al2O3). There are different clay minerals which are categorized based on presence of tetrahedral and octahedral layer in their structure like kaolinite (1:1 of tetrahedral and octahedral layers), smectite group of clay minerals (2:1 of tetrahedral and octahedral layers) and chlorite (2:1:1 of tetrahedral, octahedral and octahedral layers). The particle size of clay minerals is <2microns which can be present in form of plastic in presence of water and solidified when dried. 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Bioremediation is the most effective, economical, eco-friendly management tool to manage the polluted environment. 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This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. 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Pulsed laser deposition (PLD) results in TCO coatings with adequate optical and electrical properties, the preservation of the target chemical composition in the transferred films being the major advantage of this technique. Furthermore, the performance of the optoelectronic devices can be enhanced by patterning the TCO electrodes. Indium tin oxide (ITO) remains the most popular TCO due to its high conductivity and transparency. The scarcity of the indium resources encouraged the efforts to find an alternative to ITO, a promising candidate being Al-doped ZnO (AZO). Therefore, this chapter is focused on PLD deposition of TCO films (ITO and AZO) on patterned glass substrates prepared by ultraviolet nanoimprint lithography (UV-NIL) for obtaining transparent electrodes with improved characteristics, which further can be integrated in optoelectronic applications.",book:{id:"11484",title:"Thin Film Deposition - Fundamentals, Processes, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11484.jpg"},signatures:"Marcela Socol, Nicoleta Preda, Carmen Breazu and Oana Rasoga"},{id:"81742",title:"Collagen-Based Biomaterial as Drug Delivery Module",slug:"collagen-based-biomaterial-as-drug-delivery-module",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.103063",abstract:"In the field of medicine, controlled drug delivery has become a major challenge due to inefficiency of drug at critical parameters such as permeability, solubility, half-life, targeting ability, bio- & hemocompatibility, immunogenicity, off-target toxicity and biodegradability. Since several decades the role of drug delivery module has been a crucial parameter of research and clinical observations to improve the effectiveness of drugs. Biomaterials- natural or artificial are mainly used for medical application such as in therapeutics or in diagnostics. Among all the biomaterials, collagen based-hydrogels/ films/ composite materials have attracted the research and innovations and are the excellent objects for drug delivery, tissue engineering, wound dressings and gene therapeutics etc. due to high encapsulating capacity, mechanically strong swollen structural network and efficient mass transfer properties. Substantial developments have been performed using collagen-based drug delivery systems (DDS) to deliver biomolecules with better efficacy. In spite of significant progress, several issues at clinical trials particularly targeting of intracellular molecules such as genes is still a challenge for researchers. Experimental results, theoretical models, molecular simulations will boost the fabrication/designing of collage-based DDS, which further will enhance the understanding of controlled delivery/mechanism of therapeutics at specific targets for various disease treatments.",book:{id:"10945",title:"Collagen Biomaterials",coverURL:"https://cdn.intechopen.com/books/images_new/10945.jpg"},signatures:"Amit Kumar Verma"},{id:"82472",title:"Characterisation and Application of Nickel Cubic Boron Nitride Coating via Electroless Nickel Co-Deposition",slug:"characterisation-and-application-of-nickel-cubic-boron-nitride-coating-via-electroless-nickel-co-dep",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.105364",abstract:"The chapter describes the characterisation and application of nickel cubic boron nitride (Ni-CBN) coatings using the electroless nickel co-deposition method. Two different types of substrates were used, that is, high-speed steel (HSS) and carbide. The characterisation of Ni-CBN coating was conducted using Field Emission Scanning Electron Microscope (FESEM) JSM-7800F coupled with Energy-Dispersive X-ray (EDX). As for the application, coated end mill cutting tools were inserted into DMU 50 CNC machine to conduct the machining testing. Cutting speed, feed rate, and depth of cut were chosen for the Taguchi L9 3-level factors. Taguchi analysis was employed to determine the optimal parameters for the Ni-CBN (HSS) surface finish. The ANOVA evaluation was used to identify the most significant effect on surface finish parameters. The FESEM images prove that the nano-CBN powders were embedded in the Ni-CBN coatings and are uniformly distributed. The findings show Ni-CBN-coated tool life is 195 minutes compared to the uncoated is 143 minutes. The surface roughness, Ra values using Ni-CBN-coated tools ranges between 0.251 and 0.787 μm, whereas the uncoated tools Ra values between 0.42 and 1.154 μm. It can be concluded that Ni-CBN HSS cutting tools reduce tool wear and extend tool life. The Taguchi optimum machining condition obtained is 1860 RPM spindle speed, 334 mm/min feed rate, and 2 mm depth of cut.",book:{id:"11762",title:"Characteristics and Applications of Boron",coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg"},signatures:"Norsilawati Ngah, Nor Bahiyah Baba, Nor Azinee Said, Mohd Habir Ibrahim and Na’ain Shari"},{id:"82482",title:"Boron-Based Cluster Modeling and Simulations: Application Point of View",slug:"boron-based-cluster-modeling-and-simulations-application-point-of-view",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105828",abstract:"Among sub-nanometer clusters, boron-based clusters and their atom-doped counterparts have attracted great attention due to their mechanical, physical, and chemical properties as well as their applications. Molecular dynamics (MDs) simulations and ab initio methods, including density functional theory (DFT) calculations, have been used to understand the physical and chemical properties of different materials. Much research has recently been conducted by using various methods to determine the different properties of boron clusters. In this chapter, we briefly introduce the relevant modeling and simulation methods, then review very recent theoretical researches on the application of small boron clusters, such as gas sensors, electrodes, H2 storage, drug delivery, and catalytic applications.",book:{id:"11762",title:"Characteristics and Applications of Boron",coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg"},signatures:"Nasim Hassani, Mohammad Reza Hassani and Mehdi Neek-Amal"},{id:"82145",title:"Slope Casting Process: A Review",slug:"slope-casting-process-a-review",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.102742",abstract:"Semi solid processing is a near net shape casting process and one of the promising techniques to obtain dendritic free structure of metals. Semi solid casting gives numerous advantages than solid processing and liquid processing. Semi solid casting process gives, Laminar flow filling of die without turbulence, Lower metal temperature, Less shrinkage, Less porosity, Higher mechanical properties. Semi solid casting process is industrially successful, producing a variety of products with good quality. Slope Casting process is a simple technique to produce semi solid feed-stoke with globular microstructure and dendrite free structure castings. Slope casting process depends on different process parameters like slope length, slope angle, pouring temperature etc. The present study mainly focuses on review of various explorations made by researchers with different process parameters of the Slope casting process and explain the mechanisms that lead to microstructural changes which leads to good mechanical properties.",book:{id:"11119",title:"Casting Processes",coverURL:"https://cdn.intechopen.com/books/images_new/11119.jpg"},signatures:"Mukkollu Sambasiva Rao and Amitesh Kumar"},{id:"82363",title:"High Entropy Thin Films by Magnetron Sputtering: Deposition, Properties and Applications",slug:"high-entropy-thin-films-by-magnetron-sputtering-deposition-properties-and-applications",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.105189",abstract:"Surface coating is of a great interest to increase the performances of the materials and extend its lifetime. High entropy films (HEFs) become the hot spot for developing surface engineering applications due to their good performances. They are reported to have superior properties such as good corrosion, wear resistance and excellent high temperature oxidation. Various deposition techniques have been exploited to fabricate HEFs such as laser cladding, spraying, sputter deposition and electrochemical deposition. These techniques are known to be an easy process to achieve a rapid quenching. Magnetron sputtering is seen as the most efficient methods to deposit the HEFs. Different gas can be used to prepare the ceramic materials. Besides, the deposition parameters reveal a strong influence on the physicochemical properties of HEFs. Working pressure, substrate temperature, bias voltage and gas mixture flow ratios have been reported to influence the morphology, microstructure, and functional properties of HEFs. The chapter overviews the development of the recent HEFs prepared by magnetron sputtering technique. First, it describes the principal of the technique. Then, it reports the classes of HEFs followed by the effect of the deposition parameters on their different properties. Applications have been developed using some HEFs for biomaterials and machining process.",book:{id:"11468",title:"High Entropy Alloys - Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11468.jpg"},signatures:"Mohamed El Garah, Frederic Schuster and Frederic Sanchette"}],onlineFirstChaptersTotal:92},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:16,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:38,paginationItems:[{id:"82531",title:"Abnormal Iron Metabolism and Its Effect on Dentistry",doi:"10.5772/intechopen.104502",signatures:"Chinmayee Dahihandekar and Sweta Kale Pisulkar",slug:"abnormal-iron-metabolism-and-its-effect-on-dentistry",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Iron Metabolism - Iron a Double‐Edged Sword",coverURL:"https://cdn.intechopen.com/books/images_new/10842.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82291",title:"The Role of Oxidative Stress in the Onset and Development of Age-Related Macular Degeneration",doi:"10.5772/intechopen.105599",signatures:"Emina Čolak, Lepša Žorić, Miloš Mirković, Jana Mirković, Ilija Dragojević, Dijana Mirić, Bojana Kisić and Ljubinka Nikolić",slug:"the-role-of-oxidative-stress-in-the-onset-and-development-of-age-related-macular-degeneration",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Importance of Oxidative Stress and Antioxidant System in Health and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/11671.jpg",subseries:{id:"15",title:"Chemical Biology"}}},{id:"82195",title:"Endoplasmic Reticulum: A Hub in Lipid Homeostasis",doi:"10.5772/intechopen.105450",signatures:"Raúl Ventura and María Isabel Hernández-Alvarez",slug:"endoplasmic-reticulum-a-hub-in-lipid-homeostasis",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",subseries:{id:"14",title:"Cell and Molecular Biology"}}},{id:"82409",title:"Purinergic Signaling in Covid-19 Disease",doi:"10.5772/intechopen.105008",signatures:"Hailian Shen",slug:"purinergic-signaling-in-covid-19-disease",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Purinergic System",coverURL:"https://cdn.intechopen.com/books/images_new/10801.jpg",subseries:{id:"17",title:"Metabolism"}}}]},overviewPagePublishedBooks:{paginationCount:32,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. 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Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. 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