Daily energy production for each month in three different weather conditions.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Milk processing allows the preservation of milk for days, weeks, or months and helps to reduce food-borne illness.",isbn:"978-1-78985-730-6",printIsbn:"978-1-78985-729-0",pdfIsbn:"978-1-78985-919-5",doi:"10.5772/intechopen.73442",price:119,priceEur:129,priceUsd:155,slug:"milk-production-processing-and-marketing",numberOfPages:202,isOpenForSubmission:!1,hash:"d0b383fbc5e2a2fcc9da5bd58766529d",bookSignature:"Khalid Javed",publishedDate:"July 17th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/6911.jpg",keywords:null,numberOfDownloads:6071,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:3,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 12th 2018",dateEndSecondStepPublish:"August 2nd 2018",dateEndThirdStepPublish:"October 1st 2018",dateEndFourthStepPublish:"December 20th 2018",dateEndFifthStepPublish:"February 18th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"136829",title:"Dr.",name:"Khalid",middleName:null,surname:"Javed",slug:"khalid-javed",fullName:"Khalid Javed",profilePictureURL:"https://mts.intechopen.com/storage/users/136829/images/system/136829.jpeg",biography:"Dr. Khalid Javed is a Professor of Animal Breeding and Genetics in the Department of Livestock Production at the University of Veterinary and Animal Sciences, Lahore. He graduated in Animal Husbandry from University of Agriculture, Faisalabad in 1982. He earned his Master’s and Doctorate degrees in Animal Breeding and Genetics from University of Agriculture, Faisalabad. He joined Government of Punjab, Livestock and Dairy Development as Veterinary Officer in 1983 and remained engaged in research in different capacities. Dr. Khalid conducted research, trainings and teaching in the fields of Animal Breeding, Population/Quantitative Genetics, and Statistical Genetics. He analyzed the production data of various livestock species (e.g., cattle, buffalo, sheep, goats, chicken) to characterize the phenotypic and genetic structure related to different traits of economic importance and subsequent selection. Moreover, he has been engaged in inter-disciplinary collaborative research with colleagues from various academic and research institutes to study the genetic, breeding, management and environmental factors affecting productivity of livestock species. He joined University of Veterinary and Animal Sciences during 2003 as Assistant Professor where he was later selected and appointed as Associate Professor and Professor, in 2006 and 2011 respectively. His research focus is on selection and breeding of large and small ruminants. He also supervises and evaluates postgraduate research to ensure successful and timely completion of the projects focusing on genetic improvement, enhancing breeding efficiency and production enhancement of farm animals. In addition, he participates and conducts trainings, workshops, conferences and seminars, and writes scientific publications to disseminate knowledge and techniques to the researchers and livestock producers about various areas of animal husbandry for improving behaviour, health, growth, fertility and production of livestock. He has more than 200 publications/research articles published and is working as Senior Editor of an internationally recognized Journal of Animal and Plant Sciences-JAPS.",institutionString:"University of Veterinary and Animal Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Veterinary and Animal Sciences",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"326",title:"Food Industry",slug:"food-industry"}],chapters:[{id:"65652",title:"Current Standing and Future Challenges of Dairying in Pakistan: A Status Update",slug:"current-standing-and-future-challenges-of-dairying-in-pakistan-a-status-update",totalDownloads:1359,totalCrossrefCites:0,authors:[{id:"270832",title:"Dr.",name:"Muhammad Naeem",surname:"Tahir",slug:"muhammad-naeem-tahir",fullName:"Muhammad Naeem 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The increasing share of RES has introduced new challenges, which in future can affect the proper operation of the system: for example, the decrease of the system inertia faced by introducing new inverter controls [2]. At distribution system level, the large share of RES led researcher to consider new way to manage the system, by means of optimal reconfiguration procedure based on different methodology [3] and time periods [4]. The main drawback of RES is the intermittency of power production, which often results in a not well match between electric consumption and generation profiles [5, 6], with consequent voltage deviations and reverse power flow issues [7]. In order to reduce voltage deviations, it is possible to upgrade grid lines and transformers, but it is generally expensive. Otherwise, it is possible to reduce injection from renewable sources by increasing the supply of local loads. It can be done by load shifting, which consists of the switching on of home appliances, when PV generators are working. This procedure can be manual by using simple timed switches; for example, the user has to switch on the washing machine or the dishwasher at midday, when the production is maximum. To perform load shifting, in [8], it is developed an algorithm to predict the consumption based on hourly historical data using artificial neural networks (ANNs).
\nUsing electrochemical storage with PV generators is a good alternative to mitigate or eliminate power injection issues. Storage is easy to install and manage in any site; in the last years, the cost of storage decreases, but it is still expensive and it cannot solve the seasonal correlation between low loads and high RES production, and vice versa. For this reason, in case of domestic users, the best technical-economic solution is the use of a small battery system (BS) and the adoption of load shifting. This solution permits the reduction of absorption or injection peaks and the increase of self-sufficiency level, that is, the ratio between the local RES production used to supply loads and the total loads.
\nA battery management system (BMS) is a hardware/software solution which checks the correct operation of batteries: in its basic version, it simply charges the batteries, when they are empty, and discharges them when necessary. It limits battery operation only to protect them: the exceeds of minimum and maximum state-of-charge (SOC) limits and fast charge/discharge cycles are not permitted to avoid fast degradation [9]. An improvement in the BMS management consists of the forecast of load and PV generation profiles [10, 11]. In this case, it is necessary to have accurate information about production profiles, which are generally missing. In addition, the BMS has to continuously obtain accurate weather forecasts, which are not easily available. In [12], a modified control strategy for batteries based on peak shaving is proposed to reduce power fluctuations of production in a PV-storage system and obtain benefits in terms of electricity price. In [13], a more accurate BMS for a PV-storage system is developed: the proposed management strategy aims to shave consumptions peaks, taking into account degradation of batteries and aging limits of the storage. A real-time battery management algorithm is proposed in [14] to reduce the peak demand power and the daily energy cost in grid-connected PV-storage systems. In particular, the charge/discharge of the storage is controlled using instantaneous load data. Each day, 1-day ahead prediction of PV generation and load profiles is performed to decide the power limit beyond which the peak shaving strategy works. Finally, in [15], several control strategies of batteries are compared for a residential battery energy storage system (BESS) coupled with a PV generator. In particular, a base control strategy charges the battery when PV production exceeds local loads and starts to discharge the storage in the evening, when PV generation is negligible. It is compared to three optimized BMSs: the first one aims to maximize the economic benefits for the users or the self-sufficiency, while the second one includes utility constraints to lower overvoltage risks on distribution grid and the third is a distributed control.
\nIn the present chapter, positive aspects regarding the grid stability, i.e., frequency and voltage control [16, 17, 18], are not taken into account, and only the benefit for the users, consisting of the reduction of absorption peaks with a possible consequent reduction of contracted power, is investigated. In addition, load shifting is not considered, due to difficulties in convincing domestic users to change their habits. Indeed, a smart battery management system (SBMS), which works with raw forecasts of production and historical consumption data, is proposed: the goal of the control is to reduce the absorption peaks from the grid with minimum reduction in self-sufficiency and no load shifting. In particular, in case of high consumption and low production, a traditional BMS completely discharges the batteries and all the renewable energy is locally consumed. In the proposed SBMS, the storage will not be totally discharged and will not completely supply the loads. In fact, the storage discharge is limited to satisfy possible absorption peaks in a period up to few days. Nevertheless, if the storage is not discharged waiting for possible consumption peaks, it means that the baseload could not be satisfied with a consequent reduction of self-sufficiency. The self-sufficiency is calculated to check the effectiveness of the proposed SBMS: the domestic user has to keep high its self-sufficiency level, because it corresponds to an economic return. The benefit for the grid is not taken into account, but it exists: it consists of a reduction in peak absorption from the grid resulting in higher power quality, lower voltage dips, and reverse power flow issues [19, 20].
\nThe next sections of the chapter will be organized in the following way. In Section 2, the description of the system setup, the inputs for the simulation, and the models of the PV generator and the battery will be presented. In Section 3, the provisional energy balance and the storage management are described in detail. In Sections 4 and 5, the results of the simulations and the conclusions are discussed, respectively.
\nA scheme of a PV-storage residential system is presented in Figure 1. The main components of the power system are a PV generator, an electrochemical BMS, DC/DC and DC/AC power converters, AC loads, and the distribution grid. The PV modules are connected to a maximum power point tracker (MPPT) in order to work in the maximum power point in every irradiance and temperature condition [21]. The BMS measures DC current and voltage and temperature of batteries. The SOC is continuously calculated in order to estimate the residual charge of the storage; in this way, the BMS avoids an abnormal degradation of the batteries due to not optimal charging patterns, overcharging, undercharging, and abnormal temperatures. The DC/AC converter connects the PV system and the BESS to the AC side, i.e., local loads and the grid. Moreover, the device is Internet-connected and downloads raw weather forecast of 1-day ahead, compares provisional load and production profile, and adopts the best strategy to reduce consumption peaks.
\nThe PV-storage system under study.
The production of PV generators depends on installation conditions (location and tilt and azimuth of the PV modules) and on weather conditions (solar irradiance and temperature). The Photovoltaic Geographical Information System (PVGIS) [22] database is a free online tool; it permits to know the average daily irradiance and temperature profiles corresponding to each month of the year. The monthly profiles can be obtained for every location in Europe, Africa, and Asia starting from the definition of the location of the generator and the tilt and the azimuth of the PV modules. Additional parameters can be selected, such as the typology of solar radiation database and the calculation of irradiation profiles, also for tracking systems. In Figure 2, the home screen of PVGIS database is shown.
\nPVGIS website.
The PVGIS database provides a temperature profile and three irradiance profiles for each month. In particular, the irradiance profiles correspond to a clear sky day, an average day, and an overcast day. During the clear sky day, the global irradiation is maximum; in fact, it is mainly composed of the beam contribution, because no clouds are present. During the overcast day, the solar irradiation is minimum; in fact, in case of cloudy and rainy days, only the diffuse component of the solar irradiance is present. The average day is an intermediate situation: it is based on the average irradiance condition occurring in the month under consideration. In Figure 3, an example of the output profiles of the software is presented for January; the selected location is in Italy (Turin, 45.05° Nord, 7° 40\' Est) and the PV modules are installed with an inclination of 15° and West oriented (azimuth = 90°, where South = 0°). Data are provided with a time step of 15 min.
\nIrradiance and temperature profiles for January in Turin (Italy) from PVGIS database; PV modules have inclination of 15° and West orientation.
In the present chapter, it is supposed to install a single device including both the PV converter and the BMS; the BMS will be equipped with additional hardware and software capable of accessing Internet and download data from the PVGIS database. After the installation of the PV generator, during the setting up of the converter, the input parameters requested by PVGIS to estimate the irradiance and temperature profiles are inserted in the software of the device.
\nThe device accesses the PVGIS database and downloads and elaborates the three above-described irradiation profiles for each month. Starting from these data and the rated power of the PV generator, the power converter calculates a total of 36 PV production profiles by an appropriate photovoltaic model, which will be described in detail in Section 2.3. Finally, the power generation profiles are integrated over the entire day: the result is a list of daily energy productions for each month in three different weather conditions. Table 1 shows the daily energy production of a PV generator with rated power of 1 kWp installed as defined in Figure 3.
\nDaily energy production for each month in three different weather conditions.
Regarding the PV power simulation, the AC power production PAC is calculated according to the model described in [23]. The inputs of the model are solar irradiance G, ambient temperature Ta, and rated power of the PV generator PPV,r. The thermal losses and consequently the DC input power change, while the other sources of losses are considered constant:
\nwith
\nLosses due to temperature \n
NOCT is the normal operating cell temperature, generally provided by the manufacturer of the PV modules; in this work, it corresponds to a typical value NOCT = 45°C. GNOCT is the solar irradiance occurring at NOCT condition and it is 800 W/m2; the overtemperature losses ηtherm (with respect to TSTC = 25°C) are calculated by the formula:
\nLosses due to dirt ηdirt provide an average 2% of reduction in energy production for the deposit of dust and other materials on the glass of the modules. Thus, a typical value ηdirt = 0.98 is used in the present work [27, 28]. Note that in case of horizontal modules, the cleaning made by rain is reduced, and in case of emission of pollution close to the plants from special industrial processes [29], losses can be more than 7%. Losses due to reflection from the glass on the front of the PV module are inevitable losses due to a not ideal transparency of the glass; according to [30], they can be considered equal to ≈3% (ηrefl = 0.97). Mismatch losses are due to nonuniformity in I-V characteristics of modules connected in series or in parallel. Thus, the conversion unit imposes to the whole PV generator a working point not perfectly corresponding to the optimum. According to [31], a typical value of ηmis = 0.97 is used. Joule losses take into account dissipation of electrical energy into heat by Joule effect in the cables. During design phase, cables should be sized in order to keep Joule losses within 3% in nominal conditions [30]. Since the PV system operates at nominal conditions (maximum power) for a short period during the year, and in other conditions (partial load) losses are lower, Joule losses are estimated equal to an average value of 1% (ηcab l = 0.99) [30]. Losses for shadings are due to external causes, as a wrong design; thus, in the performed simulations, these losses are neglected (ηshad = 1). Regarding the accuracy of the maximum power point tracking (MPPT) system, it causes losses, because the optimum value is generally not perfectly tracked, especially at low power: on average, this loss can be estimated ≈1%. (ηMPPT = 0.99) [32]. Finally, the DC/AC conversion introduces losses, which are quadratically dependent on the power output. For the sake of simplicity, an average value of ηDC/AC = 0.97 is considered in the present work [33].
\nA correct model of the storage is fundamental to evaluate energy flows. Many electric models are present in literature and they permit to simulate operation of batteries with different pros and cons [34, 35, 36, 37]. The simplest model describes a battery by an equivalent voltage source in series with an internal resistance. The equivalent voltage can easily be determined by measuring the open circuit voltage of the battery, while the measurement of the internal resistance requires a further test performed during battery charge. Obviously, this model has a limited use, because the parameters are constant: the accumulator results in having an infinite capacity and there is no way to determine the SOC. An upgrade with respect to the basic model is obtained using an equivalent resistive-capacitive model [36]. The values of resistances and capacitances can be determined through impulsive test of the battery. The advantage of this model is that it permits to evaluate the charge and discharge transients with variable loads in time. However, the SOC dependence on the voltage, which has to be determined, requires careful preliminary measurements on the battery. Another possible model consists of the impedance model, where a voltage source is in series with a resistance and an inductance. An additional series impedance is used to represent the electrochemical characteristics of the battery. Nevertheless, the definition of this impedance is complicated; in fact, it can be obtained starting from an electrochemical impedance spectroscopy to obtain an equivalent impedance in the frequency domain. In addition, the impedance has to be characterized varying the state of charge and the temperature [35].
\nThe most sophisticated models [38, 39] are developed to calculate also the state of health (SOH), which is a parameter useful to evaluate how the charge-discharge profiles affect the storage life and when the batteries have to be replaced. In fact, PV production is intermittent; thus, PV generators cannot guarantee the optimal charge-discharge cycles to have the longest possible life of storage and the highest efficiency. For example, the real-time model described in [39] is a blend of the previous battery models whose particular combination of components and dependencies eases the estimation of the equivalent parameters. In conclusion, this model permits to calculate the SOC, the SOH, and then the residual life. This information permits the evaluation of the economic investment of electrochemical storage system [40], taking in consideration the battery management. Nevertheless, the models that permit to estimate the SOH require a continuous measurement of battery parameters (i.e., voltage, current, and temperature of the batteries) [37]. For this reason, the calculation of the SOH cannot be performed with only simulations, but a real system with continuous measurements is required.
\nThe energy model is used in the simulations presented in this chapter, because it permits to simulate the SOC with a good approximation (a few percent points) without measurements and with a low computation effort (only the formulas (5) and (6) are used). The energy model permits to estimate the state of charge of batteries; i.e., how much energy is stored or can be stored in a battery with rated energy capacity Cbat, by the comparison with the limits imposed to preserve life of batteries. The calculation of the SOC(t) at the instant t is a function of the state of charge SOC(t − 1) at the previous time step, of the power exchanged Pbat during the time step Δt (in this chapter, Δt = 1 min) and of the charge efficiency ηbat. During the charge phase, the batteries behave as a generator (Pbat > 0) and it is considered a charge efficiency ηbat = 0.88; during discharge (Pbat < 0), efficiency is considered unitary.
\nThe proposed BMS periodically defines the strategy to minimize the power absorption from the grid. The strategy selection is performed two times per day to better match the consumption peaks of domestic users, which occur early in the morning and during the evening. Thus, the day is divided in three time slots. The first time slot starts at midnight and ends at 6:00 a.m. Between 6:00 a.m. and 6:00 p.m., there is the second time slot: the production is dominant and in case of people at home, part of generation is self-consumed. In this period, the consumption peak in the morning due to preparation to work and school activity (such as hairdryers, electric boiler, etc.) is included. Obviously, this peak cannot be totally satisfied by PV production, especially in winter. The third time slot starts at 6:00 p.m. and finishes at midnight, when the second consumption peak occurs, and PV production is low or negligible.
\nThe time 6:00 p.m. is selected for the download of raw weather forecasts for the next 24 h, for the calculation of provisional energy balance and the update of management strategy for batteries. In fact, at 6:00 p.m., the PV production is almost over: the BESS can accurately calculate the quantity of stored energy, which will be available for the next hours. In fact, during evening and night, the batteries will not be charged: supply from the grid is not considered.
\nThe provisional energy balance for 1-day ahead is performed comparing estimated energy production and consumption. Regarding the energy consumption, this value is calculated on the basis of measurement of local consumption profiles. Loads are monitored, and average values of energy consumption are calculated for each of the three time slots composing the day, as described in the previous paragraph. In addition, a distinction of average energy consumption between working days and holidays is considered.
\nRegarding the provisional production, every day at 6:00 p.m., the converter downloads raw weather forecasts for the next 24 h. Data are collected from commercial web services: they generally identify weather forecast with simplified symbols, i.e., showing a sun symbol for a clear sky day and lightning for rain. For the sake of simplicity, in the present work, it is considered a three-level forecast: a clear sky day, an average day with few clouds, and a cloudy/rainy day. These levels correspond to the three irradiance conditions provided by the database PVGIS. In this way, it is defined a raw correlation between the weather forecast and the expected production from the PV generator. The advantage consists of a free and easily accessible daily forecast of production, which can be used for free by the Internet-connected BESS to select the best battery management.
\nThe first step in the smart management of batteries consists of the definition of the total discharge time (TDT): Figure 4 shows the flowchart of the procedure. First, at 6:00 p.m., after weather forecast download, the provisional balance between expected production EPV_1day-ahead and loads Eloads_1day-ahead, 6 a.m.-6 p.m. occurring in the time slot 6:00 a.m.–6:00 p.m. of the day ahead is performed.
\nDefinition of the total discharge time (TDT).
In case of PV energy production higher than loads EPV_1day-ahead > Eloads_1day-ahead, 6 a.m.–6 p.m., a management of the storage to satisfy loads until 1-day ahead at 6:00 a.m. is performed. In this case, the TDT will be equal to 12 h. In fact, the day after, during light hours, energy will be self-consumed, and the surplus of PV production will charge the storage or will be injected into the grid. Vice versa, in case of low production and high loads EPV_1day-ahead < Eloads_1day-ahead, 6 a.m.–6 p.m., an SBMS is necessary not only for 1-day head but also for the day after. In this case, the PV production cannot satisfy local loads and storage has to be able to reduce loads for two nights, and the TDT will be equal to 36 h.
\nBatteries are expensive [40], and considering a storage with a too high capacity is not cost-effective for a grid-connected plant. For this reason, in the present work, the BESS can distribute the stored energy in a maximum TDT = 36 h. It means that storage must be able to supply the load when a single cloudy day occurs (2 nights and 1 day).
\nFigure 5 shows an example of PV and load profiles for 2 days: in the first day, the PV production is low, while the second one is a clear sky day. At 6:00 p.m. of day #1, the procedure starts with the converter downloading forecast for day #2: supposing a correct forecast, the result is a provisional high PV production. Thus, the BESS will manage the discharge of the storage from the evening of day #1 at 6:00 p.m. to the morning of day #2 at 6:00 a.m. (12 h). After 6:00 a.m. of day #2, storage and loads will again be mainly supplied by the PV production.
\nExample of PV and load profiles for 2 days.
The second case is shown in Figure 6. It presents an example of PV and load profiles for 3 days: in the first and second days, the real PV production is low, while the third one is a clear sky day. At 6:00 p.m. of day #1, the converter downloads forecast for day #2: supposing a correct forecast, the result is a provisional low PV production. Thus, the BESS will manage the discharge of the storage until the morning of day #3 (a total of 36 h, from 18 to 54 h in Figure 6).
\nExample of PV and load profiles for 3 days.
After the definition of the total discharge time (TDT), the procedure continues with the second part; i.e., the definition of the storage management strategy. The SOC is calculated at 6:00 p.m. by the BESS, which uses appropriate models starting from the real-time measurement of voltage and ambient temperature of batteries, as described in Section 2.1. The rated capacity of the storage and the SOC permit to calculate the energy that can be provided to the loads Ebatt,disch. The estimated energy production EPV_1day-ahead is the same quantity used in the previous step, while the consumption Eload,TDT corresponds to the estimated loads during the TDT (Figure 7). These raw energy quantities are compared and it is defined if there is an energy deficit EPV_1day-ahead + Ebatt,disch ≥ Eload,TDT or surplus EPV_1day-ahead + Ebatt,disch < Eload,TDT.
\nDefinition of storage management strategy.
If the PV production and the storage can satisfy the load EPV_1day-ahead + Ebatt,disch ≥ Eload,TDT in the selected TDT, no advanced management of the batteries is required (BMS Strategy #1).
\nOn the contrary, if loads are too high EPV_1day-ahead + Ebatt,disch < Eload,TDT, peak shaving strategy (BMS Strategy #2) or appropriate discharge profiles (BMS Strategy #3) are adopted. To select the most appropriate method between BMS Strategy #2 and BMS Strategy #3, a provisional self-sufficiency Rsuff parameter, that is, the ratio between the provisional PV production plus the available energy from the battery, and the provisional local loads, is calculated:
\nWhen the ratio Rsuff is lower than a user-defined threshold Rthres, the BMS Strategy #2 is adopted: the local generators and the storage will provide a low quantity of energy to the loads, which will be mainly supplied by the grid. It can result in high absorption peaks. In this case, the low energy quantity stored in the batteries will be used only when loads exceed a maximum limit Pload,max, such as the contracted power absorption limit or another user-defined threshold. The BMS Strategy #3 is adopted when the ratio Rsuff is higher than the user-defined threshold Rthres and lower than unit value. This case is better than the previous one, because great part of loads will be supplied by PV and storage and the quote from the grid is low.
\nThe storage management strategies consist of peak shaving and of a time-dependent discharge profile. According to the procedure described in the previous subsection, when the storage energy is much lower than loads, only the peak shaving technique is adopted (BMS Strategy #2). Thus, batteries are discharged only when strictly necessary, i.e., when a load peak occurs. In particular, storage will be discharged only by the quota exceeding an user-defined limit Pload,max.
\nIn the other case, if the energy stored in the batteries is slightly lower than loads, the charge is used both for baseload supply and peak shaving (BMS Strategy #3). Nevertheless, the exact time schedule of loads is not predictable and it is not possible to know when the load peaks will occur. In the worst case, storage will be discharged soon in the evening, while the peak will be in the next early morning, when batteries are already empty. For this reason, the SBMS limits the discharge of batteries during time with the definition of different levels of minimum SOCmin,x for an user-defined number of time slots x, in which the TDT is divided. According to the procedure proposed in Section 3.2, in case of TDT = 12 h, the number of time slots x = 2, otherwise with TDT = 36 h, the time slots are 5 (x = 5). The SOCmin,x limits are defined in order to distribute the stored energy proportionally to the provisional energy consumption. Thus, SOCmin,x limits are calculated starting from the SOC of the storage, measured in real time by the BMS, and the provisional energy consumptions:
\nwhere Eload, slot x is the provisional energy that will be required by loads in the time slot x. For example, let us suppose that the TDT is 12 h and the overall required load will be 10 kWh. In particular, during the evening (from 6:00 p.m. to midnight), the required load will be 4 kWh, and during the next night (from midnight to 6:00 a.m.), the load will be 6 kWh. The stored energy will be discharged as follows: 40% during the evening and 60% during the night. In this example, the storage is considered initially full and with a minimum SOCmin,safety = 0.2.
\nFigure 8 shows an example of load and SOC profiles in case of a basic battery management. In this case, the storage is charged when PV production is higher than loads and batteries are empty; on the contrary, storage is discharged if PV production is lower than loads [23]. The only limitation in charge/discharge is performed to avoid fast degradation of batteries, by limiting the SOC in a safety range SOCmin,safety < SOC < SOCmax,safety. For sake of simplicity, it is considered a rainy day and the production from the PV generator is negligible. In case of lithium batteries (Figure 8), the minimum level SOCmin,safety generally corresponds to SOCsafety ≈ 20%, while in case of lead-acid batteries, it can reach 50% [41, 42]. In the example of Figure 8, the storage supplies the loads until 10:50 a.m., when the SOCmin,safety is reached. After that, only the grid supplies the load and the highest absorption peak is not limited ≈2.9 kW.
\nExample of load and SOC profiles in case of basic BMS.
Figure 9 shows an example of load and SOC profiles in case of the SBMS, which reduces the absorption peaks from the grid. In this case, the SOC cannot drop down under a temporary minimum SOCmin,a = 75% before midnight; then, the discharge is limited by SOCmin,b = 70% between midnight and 06:00 a.m. Between 06:00 a.m. and 06:00 p.m., the minimum admitted SOCmin,c is 37%. Then, between 06:00 p.m. and midnight, the limit SOCmin,d = 22%. Finally, after 06:00 p.m., the last limit corresponds to the same level of the basic management SOCsafety ≈ 20%, which is a typical value to preserve life of lithium batteries. The main difference from the basic management consists of a small reserve in storage, which is always present, and the absorption peaks are always reduced. On the other hand, preserving the storage partially charged could reduce the self-sufficiency. The best solution consists of the abovementioned SOC levels selected to reduce absorption peak and keep as high as possible the self-sufficiency level.
\nExample of load and SOC profiles in case of the proposed SBMS.
Simulations of the PV-storage system are performed for the entire month of December with a 1-min time step for both basic and proposed BMS to compare their performance. During winter, the PV production is low, batteries are often empty, and the development of an efficient BMS is necessary to reduce the absorption peaks from the grid. On the contrary, in summer, PV generation generally charges storage and directly supplies part of the loads.
\nThe optimal management of the storage is investigated in case of different sizes of the PV system PPV,r and different capacities of the battery Cbat. Regarding PPV,r, it ranges between 2 and 6 kWp with a step of 1 kW, while the storage capacity Cbat is in the range 1–5 kWh (step of 1 kWh). The management parameters are the power value Pload,max beyond which the peak shaving strategy works and the threshold Rthres. The power limitation Pload,max ranges between 0.5 and 2 kW with a step of 0.5 kW, while the user-defined threshold Rthres varies between 50 and 80% (step of 10%). Regarding the loads, the measured consumption profile of a domestic user (a family composed of two persons) located in Northern Italy (45.05° Nord, 7° 40\' Est) is used. The annual consumption of the domestic user analyzed in the case study is ≈2800 kWh/year and its loads correspond to typical home appliances (e.g., hairdryer, oven, personal computer, lighting, and electric water heater).
\nThe results of the simulation show that the proposed BMS decreases the peaks of absorption from the grid with respect to a traditional management. The results are interesting especially in case of a small storage, while in case of higher storage capacity, there are negligible differences between the two managements. Figure 10 shows case #1: it corresponds to the analysis of 2 days of simulation for a PV system with PPV,r = 4 kW and a storage system with Cbat = 2 kWh. In the graphs, in case of battery discharge, the sign of the power supplied by the storage to the loads is negative. The 2 days are characterized by cloudy and rainy conditions, and the PV production is low. The proposed BMS calculates the provisional energy balance and a huge lack in storage is predicted; thus, the peak shaving method is used (BMS Strategy #2). Before 6:00 p.m., all the loads are supplied by PV and storage; then, peak shaving is applied and only the quota exceeding Pload,max = 2 kW is satisfied by batteries. The saved energy is then preserved and used to shave loads during the second day, with the result of keeping the absorption from the grid always ≤2 kW.
\nPower profiles for case #1 with the proposed SBMS.
On the contrary, if a standard BMS is used (Figure 11), all the stored energy is consumed before the end of the evening of the first day; furthermore, there is no energy from storage to supply the load peaks during the second day. The result is a maximum absorption peak of ≈4.2 kW: during these days, the proposed SBMS reduces the absorption peak of ≈50%.
\nPower profiles for case #1 with standard BMS.
Table 2 shows the energy balance of the case #1 related to Figures 10 and 11. With the proposed SBMS, the maximum power absorbed from the grid is half, while the deviations in terms of self-sufficiency and injected energy into the grid are negligible. Nevertheless, there is an increase in grid absorption: to guarantee power for peak shaving, a residual energy is kept in the storage, and at 6:00 p.m. of the second day SOC≈0.7.
\n\n | Proposed BMS | \nStandard BMS | \n
---|---|---|
Load (kWh) | \n11.45 | \n11.45 | \n
Self-consumption (kWh) | \n3.2 | \n3.2 | \n
Grid absorption (kWh) | \n5.5 | \n4.58 | \n
Grid injection (kWh) | \n0.52 | \n0.52 | \n
Self-sufficiency/load (%) | \n28 | \n28 | \n
Self-consumption/PV production (%) | \n43 | \n43 | \n
Grid injection/load (%) | \n4.5 | \n4.5 | \n
Pload,max (kW) | \n2 | \n4.22 | \n
Injection peak (kW) | \n−0.81 | \n−0.81 | \n
Energy results for case #1.
A second simulation is shown in Figure 12. The case #2 is characterized by two different days with respect to case #1: a negligible PV production occurs in both days, while the sizes of PV and storage systems and loads are the same of case #1. The provisional energy balance predicts that the energy in the storage will supply great part of the loads, but it will be not sufficient to supply them totally. The provisional self-sufficiency parameter is Rsuff > Rthres (with Rthres = 50%); thus, the converter selects the BMS Strategy #3. The most interesting part corresponds to the time window 6:00 a.m.–6:00 p.m. of the second day. Batteries start discharging at 6:00 a.m. and when peaks occur (10:00–12:00 a.m.), only the quota exceeding Pload,max = 2 kW is satisfied by batteries. In the same way, the other absorption peak occurring at 9:00 p.m. is shaved, thanks to the preserved energy in the storage. The maximum absorption peak is 2.4 kW.
\nPower profiles for case #2 with proposed SBMS.
On the contrary, in the same conditions, a traditional BMS would discharge the battery before 10:00 a.m. and the absorption peak would be 2.8 kW (higher than the proposed SBMS of ≈14%).
\nFinally, in Table 3, the above-described combination #A and other three combinations of PV and storage sizes, which permit to obtain significant improvements, are presented. The power and energy results of the proposed SBMS are compared to the standard BMS. In all the other cases, the improvement in terms of maximum absorption from the grid is confirmed, ranging from ~9 to ~10%. Regarding the maximum injection into the grid and the energy quantities, their deviations are negligible. The combination #A shows much better results, confirming that the performance of the proposed SBMS increases when the PV system size is high and when the storage is undersized. In addition, a low value of Rsuff permits to increase the use of peak shaving, without affecting the energy balance.
\nComb-inations | \nPPV,r (kW) | \nCbat (kWh) | \nPload,max (kW) | \nRthres (%) | \nStandard BMS Pmax,absorbed (kW) | \nProposed BMS Pmax,absorbed (kW) | \nImprove-ment (%) | \n
---|---|---|---|---|---|---|---|
#A | \n4 | \n2 | \n2 | \n50 | \n4.22 | \n2.56 | \n39 | \n
#B | \n2 | \n1 | \n2 | \n70 | \n4.42 | \n4 | \n9.5 | \n
#C | \n3 | \n2 | \n2 | \n80 | \n4.33 | \n3.91 | \n9.7 | \n
#D | \n5 | \n5 | \n1 | \n60 | \n4.10 | \n3.74 | \n8.8 | \n
Results of the alternative configurations.
In the present work, smart BMS for residential users with a grid-connected PV-storage system is proposed. The BMS is Internet-connected and it downloads 1-day ahead weather forecasts, which are used to obtain a provisional energy production for the PV generator. These data are compared with load estimations, based on historical data. The result is a provisional energy balance, which is used by the BMS to select the best strategy to discharge batteries. In particular, the BMS preserves battery charge, when high load and low production is expected, and performs peak shaving, when loads exceed a user-defined limit. The combination of these methods results in a reduction in absorption peaks from the grid, with negligible variations in terms of self-sufficiency. The proposed BMS is efficient in case of undersized batteries, where the energy available in the storage is often not sufficient to supply all the loads. For example, in case of a family composed of two persons with a PV plant with rated power 4 kW and a storage of 2 kWh, the reduction in absorption peak from the grid during winter days varies from 39 to 50%. Other combinations of PV and storage sizes are investigated and improvements in terms of peaks reduction are generally around 10%.
\nAcronyms | |
ANN | artificial neural networks |
BS | battery system |
BMS | battery management system |
BESS | battery energy storage system |
MPPT | maximum power point tracker |
PV | photovoltaic |
PVGIS | Photovoltaic Geographical Information System |
RES | renewable energy sources |
SBMS | smart battery management system |
STC | standard test conditions |
Symbols | |
γth | temperature factor of power of PV generator (%/°C) |
ηcabl | Joule losses |
ηcharge | charge efficiency of the battery |
ηDC/AC | DC/AC conversion losses |
ηdirt | losses due to dirt |
ηmis | losses due to mismatch |
ηmix | global losses of PV generator |
ηMPPT | DC/DC conversion losses |
ηrefl | losses due to reflection |
ηshad | losses due to shadings |
ηtherm | thermal losses of PV generator |
Cbat | rated capacity of the battery (kWh) |
Ebatt,disch | battery energy provided to the loads (kWh) |
Eload | energy consumptions (kWh) |
Eloads_1day-ahead, 6 a.m.–6 p.m. | 1-day ahead expected loads in the time slot 6 a.m.–6 p.m. (kWh) |
Eload, slot_x | provisional loads in the time slot x (kWh) |
Eload,TDT | estimated loads during the TDT (kWh) |
EPV | PV production (kWh) |
EPV_1day-ahead | 1-day ahead expected PV production (kWh) |
G | solar irradiance (W/m2) |
GNOCT | solar Irradiance at NOCT conditions (W/m2) |
NOCT | nominal operating cell temperature (°C) |
PAC | AC power production of PV generator (kW) |
Pbat | power exchanged by the battery (kW) |
Pload,max | maximum load power satisfied by the grid in case of peak shaving strategy (kW) |
Pmax,absorbed | maximum power absorbed from the grid (kW) |
PPV,r | rated power of PV generator (kW) |
Rsuff | provisional self-sufficiency parameter |
Rthre | threshold for the parameter Rsuff |
SOC | state of charge of the battery |
SOCmax,safety | maximum safety limit of SOC |
SOCmin,a | minimum SOC in the time slot a |
SOCmin,b | minimum SOC in the time slot b |
SOCmin,c | minimum SOC in the time slot c |
SOCmin,safety | minimum safety value of SOC |
SOCmin,slot_x | minimum SOC in the time slot x |
SOH | state of health of the battery |
Δt | simulation time step (min) |
t | simulation time (min) |
Ta | ambient temperature (°C) |
Tc | temperature of PV cells (°C) |
TSTC | temperature at standard test conditions (°C) |
TDT | total discharge time of the battery (h) |
x | user-defined number of time slots in which the TDT is divided |
Pests and diseases have always had repercussions directly on losses of crops and livestock products and indirectly over the income decreases due to insufficient harvests of commercial crops. Chemical pesticides are used in an excessive way and without prior technical assistance to pest distribute control, which instead of solving the problem, has produced strong damage to agricultural and livestock productivity, as well as important environmental effects with implications to public health [1]. Pesticides are chemical substances designed to prevent, delay, repel or fight any pests [2].
In [3], it was mentioned that Mexico ranked fifth in the world in the use of 1,1-bis(4-chlorophenyl)-2,2,2-trichloroethane (DDT) in agricultural programs and fourth place for its use in public health. As many as 69,545 ton of DDT were used in health campaigns for the control of malaria and agricultural activities, from 1957 onward, DDT was applied every 6 months indoors and outdoors with a coverage of 2 g/m2, and almost 1000 ton DDT/y were used in agricultural areas [4]. Indeed, DDT was used in Mexico until the year 2000, and DDT and its metabolites have been found in the environment [5] as well as in human tissues [5, 6], breast milk [7], raw cow’s milk and bovine meat [8, 9]. DDT is a very stable organochlorine pesticide that is almost completely metabolized, but small percentage remains as o,p´-DDT, while the most of its concentration is transformed into p,p´-DDE, which is characterized for its poor solubility in water and high affinity for lipids. It is considered a pollutant of high persistence due to its half-life of up to 15 years in the environment [10]. This pesticide persistence is responsible of the wild flora and fauna deterioration, as well as the contamination of soil, water table, continental, and coastal waters. Besides, pesticides can be incorporate into pasture, vegetables, and edible animals, which when consumed, act as transporters facilitating its accumulation in living organisms [11, 12].
In the state of Chiapas, México, [13] found high levels of total DDT in outdoor soil samples that ranged from 0.002 to 27 mg kg−1, while the levels found by [4] in Chihuahua, México, ranged from 0.001 to 0.788 mg kg−1. Taking into account the guideline for total DDT in residential soil of 0.7 mg kg−1 in Canada [14], the soil samples from Chiapas had levels higher than the guideline. The high levels of OCs observed may be due to ongoing usage as well as the emission of old residues from soil. Soils are an important sink and source for persistent organic pollutants to the atmosphere. In many of the cases, the contamination of soil with pesticides is due to its incorrect storage, either by leakage of corroded tanks containing liquid pesticides or by aerial dissemination of powder pesticide. However, when pesticides infiltrate the soil, their dissemination depends on the nature of the pesticide, as well as the composition, moisture, pH, and temperature [12, 15]. Because of that, a small portion of spilled pesticides can generate a high soil contamination. Moreover, soil pesticides infiltration can cause their introduction and distribution in the food chain, accumulating successively on each ecological niche until reaching lethal doses for some constituent organisms of the chain, or until reaching high levels of the trophic network [16].
This problem is aggravated due to the excessive use of pesticides in the agricultural sector, the absence of technological remediation and the lack of safety interval (waiting period) for the harvest of agricultural products, which has significant impacts on public health [17]. Some of the toxic effects of DDT and its metabolite identified in mammalian have been alterations in fetal development and adverse effects on testicular function (semen, sperm, and sperm motility decrease) due to the mimetization or antagonism of reproductive hormones [18, 19]. Thus, DDT metabolites and DDT are considered endocrine disruptors, with estrogenic properties related to several types of estrogen-dependent cancers, such as breast cancer; therefore, the use of this pesticide was prohibited on the decade of the 1970s in most countries [20]. Nevertheless, pesticides remain in the environment (persistence); therefore, the pesticides have been widely distributed, and their traces can be detected in all areas of the environment (air, water, and soil) [21, 22]; therefore, current tendency is focused on natural sources for biological pesticide control. On the other hand, the indiscriminate and uncontrolled application of synthetic pesticide besides to accumulate residues in the environment and, in some cases, in living beings, has caused resistance in some pest. The first case reported of DDT resistance occurred in 1947, and since then, it has increased alarmingly, and it has been estimated that there are currently around 489 species of pest resistant to 400 different pesticides in the world [23]. The irrational use of synthetic pesticide has produced genotypic and phenotypic changes in many species, generating resistance to the action of most of them, including inorganics, DDT, cyclodienes, organophosphates, carbamates, pyrethroids, juvenile hormone analogues, avermectins, neonicotinoids, and antimicrobial [22, 23, 24].
Though the use of pesticides has offered significant economic benefits by enhancing the production and yield of food and fibers and the prevention of vector-borne diseases, evidence suggests that their use has adversely affected the health of human populations and the environment [21]. Because of this problem, several researchers are focused their studies on the identification of new natural sources containing active metabolites that could be used on the control of pest [25, 26].
The development of essential oils (EOs) as plant protection products is especially suited to organic farming as well as to integrated pest management. They are natural in origin and biodegradable, have diverse physiological targets within insects, and, consequently, may delay the evolution of insect resistance [27]. EOs act as fumigants, pesticides, repellents, and antifeeds that could affect some biological parameters such as grown rate, biological cycle, and reproduction [26]. One of the most widely analyzed oils is neem (Azadirachta indica) which has a toxic effect over several pests and it is a potential alternative to the synthetic pesticide [28]. Neem oil active compounds are azadirachtin, salannin, nimbin, and their respective analogues; being the azadirachtin the most abundant compound [29]. Azadirachtin acts on the immature stages of the insects avoiding their molt or maturation from larva to pupa and generating mutations in the development of different essential parts for their survival, because it affects their ability to oviposit in mature stage and hatch during the larval stage [30]. Its effect is reinforced by the action from the rest of limonoides, such as salannin and nimbin, which have repellent and antifeeder effects over many insects [31]. Although the concentration of azadirachtin is sufficient and its location well established in the seed, its extraction presents some problems, because it is soluble in polar organic solvents, but slightly soluble in water, besides is photosensitive and thermolabile, which conditions its activity in the oil.
Among the methods used for the extraction of azadirachtin, it could be mentioned the cold extrusion in a mechanical press, maceration, and percolation with the use of organic solvents. Each of the proposed methods stimulates the extraction of azadirachtin but in different proportions. Various researchers have suggested that the high variability in the extraction of azadirachtin from neem depends on several factors as age of the tree, region of its production, stage of fruit development, availability of the internal portion of the seed, storage conditions of the seed, methods and solvents used for its extraction, and the particle size [32, 33, 34, 35]. However, one aspect that has not been taken into account is the physical barrier exerted by the cell wall of the seed that directly affects the availability and extraction of azadirachtin and other acaricidal compounds. This problem has been overcome with great success in the plant extract industry through the application of cellulases or preparations with multiple enzymatic activities (cellulases, hemicellulases, and pectinases) [34, 36, 37].
The assisted extraction by cellulolytic enzymes has proven to be a viable and feasible tool to obtain bioactive metabolites from plants, due to its effect on lignocellulosic structures of its cell wall, which increase the yield of oils, pigments, flavorings, and aromas extracted in comparison with traditional extraction methods [38]. Due to the advantages of the use of cellulolytic enzymes for the production of bioactive metabolites from plants and the inherent need to develop sustainable and environmentally friendly alternatives that avoid the resistance phenomenon, the present study had the purpose of evaluating the use of food-grade enzyme preparation on the hydrolysis of the neem seed to obtain extracts with higher concentrations of azadirachtin, but without the use of solvents.
The study was divided into two stages: (1) determination of optimum activity conditions of four enzyme preparations and (2) evaluation of the azadirachtin release kinetics under optimal conditions of enzyme preparation activities. The enzyme preparations used in this study were Crystalzyme PML-MX, Cellulase 17600, Crystalzyme Cran, and Crystalzyme 100XL, and their optimum pH and temperature conditions were identified by using dehydrated and pulverized neem seed. Then, four volumes (1, 2, 3, and 4 mL) of each enzyme were tested to determine the optimum enzyme concentration to neem seed hydrolysis.
Evaluations of azadirachtin release kinetics were conducted at optimum pH and temperature for each enzyme preparation, and the maximum time required for the azadirachtin extraction was determined.
Neem seeds were provided by a local producer from Jamapa, Veracruz during June, 2017. One kilogram of 110–120 day old neem seeds (green-yellow coloration) was collected. Harvested seeds were washed and cut in half to extract the cotyledon extraction, which was stored by in freezing at −20°C for 24 h before using.
Each preparation enzymatic activity was measured by filter paper test [39], where the activity was defined as filter paper units per enzyme milliliter (FPU.mL−1). After FPU.mL−1 determination, the effect of the enzyme concentration on the hydrolysis of the neem seed cellulose structures and the azadirachtin release was evaluated. Neem seed hydrolysis was indirectly determined by the quantification of reducing sugar released during the enzymatic reaction. Total protein was estimated with [40] bovine serum albumin (BSA) as standard, and enzymatic activity (EA) for each enzymatic preparation was defined as changes in absorbance in 0.001 of reducing sugars mg protein−1 min−1.
The optimal conditions of pH and temperature were established based on the enzymatic hydrolysis of the neem seed and the release of azadirachtin. To determine the optimum pH of the enzymatic preparations, 1 g of the dehydrated seed was homogenized with 19 mL of phosphate buffer (3.0, 3.5, 4.0, 4.5, 5.0, and 5.5) and was conditioned at 50°C for 5 min and after 1 mL of the corresponding enzyme preparation was added. The enzymatic reaction was carried out for 2 h and was stopped by immersion in ice water. Subsequently, 1 mL of the sample was taken for reducing sugar determination and 1 mL for azadirachtin quantification by HPLC [41]. Once the optimum pH of each enzyme preparation was identified, the effect of the temperature at 25, 30, 35, 40, 45, 50, 55, 60, 65, and 70°C was evaluated. Additionally, one extract without enzyme added was prepared as control, and the concentrations of reducing sugars released were subtracted from the values obtained in each of the enzymatic treatments. Furthermore, enzyme:neem seed (dry base) ratio was evaluated under optimal conditions of pH and temperature, and for this, 2 g of neem seeds (wet base) were used and 0.5, 1.0, 2.0, and 4 mL of each of the enzyme preparations were tested.
To determine kinetics of azadirachtin release, 100 g of neem seed (wet base) was homogenized with phosphate buffer at the optimum pH previously identify (1:10, w v−1) ratio for 3 min using an Ultra Turray homogenizer, T-25 basic (IKA®, Wilmington, NC). Five extracts were elaborated, one for each enzyme preparation and one without enzyme (control), and were incubated at optimum temperatures for each one. An aliquot was taken at 0, 2, 4, 6, 12, 18, and 24 h for the determination of azadirachtin. All samples were analyzed by triplicate.
Azadirachtin quantification was carried out by the HPLC technique proposed by [41], using a binary HPLC system (Waters 1525) and a photodiode detector (Waters 2996). The analytical separation was carried out with a Nova-Pak C18 column of 4 μm (3.9 × 150 mm) SUM (Waters® Milford, MA). Neem samples were centrifuged and diluted with acetonitrile (1:1, v v−1) before analyzing by HPLC. The samples were filtered through acrodiscs (Millipore) of 0.22 μm, and 20 μl was injected into the column. The flow rate was set at 1 mL min−1, the mobile phase was acetonitrile: water (40:60, v v−1), and azadirachtin was read at 217 nm in a retention time of 3.1 min.
The optimal conditions of pH, temperature, and the enzyme:substrate ratio were analyzed by means of analysis of variance (ANOVA) at a level of significance of p < 0.05, and Tukey’s tests were used to evaluate the differences between means with the statistical program Minitab 17.3. The kinetics of release of reducing sugars and azadirachtin were analyzed by a first-order empirical equation Yi = Ye (1−e−kt), in which the equilibrium concentrations (Ye) and its release rate constant (k) were determined, and the results were analyzed by ANOVA (p < 0.05) to determine differences between treatments.
In order to achieve the optimum cellulolytic activity of the enzyme preparation, filter paper units (FPU.mL−1) of each one were first determined. Crystalzyme PML-MX and Cellulase 17600 L showed the highest cellulolytic activities with 6.96 and 5.60 FPU.mL−1, respectively. This result is probably due to the presence of cellulases in these enzymatic preparations, since the determination of the activity is carried out with filter paper, which is formed by cellulose [42], while the enzymatic preparations, Crystalzyme 100XL and Crystalzyme Cran, showed lower activities with 2.65 and 2.38 FPU mL−1, respectively, and do not contain cellulases (Table 1). These results were used as reference to define the amount of enzyme needed to hydrolyze the neem seed cellulolytic structures and extract the highest concentration of azadirachtin.
Enzyme preparations | Cellulolytic activity | Activity (FPU mL−1) |
---|---|---|
Crystalzyme PML-MX | Pectinase, endoglucanase, exoglucanase, hemicellulase | 6.96 |
Crystalzyme Cran | Pectinase | 2.38 |
Crystalzyme 100XL | Pectinase and arabinase | 2.65 |
Cellulase 17600 | Endoglucanase, exoglucanase, β-glucosidase, pectinase and arabinoxylanase | 5.6 |
Enzymatic activities of the food-grade enzyme preparations.
Determination of the optimal reaction conditions was carried out at 50°C, taking into account that temperature is indicated by the enzyme preparation supplier as the optimum. However, in the evaluations, dehydrated neem seed was used instead of filter paper or crystalline cellulose. Figure 1 shows the determinations of the optimum pH of each enzyme preparations, and the results indicate that all enzyme preparations perform the hydrolysis of the neem seed under very similar pH conditions regardless of the combinations of cellulolytic activity they contain. Optimal pH of the enzymatic preparations was 5.0 for Crystalzyme Cran and 4.5 for Crystalzyme PML-MX, Cellulase and Crystalzyme 100XL. Of the four enzyme preparations, Crystalzyme PML-MX exhibited the highest hydrolysis of the neem seed with 2.2114 (± 0.1879) mg reducing sugars mL−1extract. A second reaction was carried out adjusting each hydrolysis to the optimum pH of each enzyme, and the effect of the temperature in the range of 25–70°C was evaluated (Figure 2).
Optimum pH of food-grade enzyme preparations: (A) Crystalzyme PML-MX, (B) Cellulase 17600, (C) Crystalzyme Cran, and (D) Crystalzyme 100XL.
Optimum temperature of food-grade enzyme preparations: (A) Crystalzyme PML-MX, (B) Cellulase 17600, (C) Crystalzyme Cran, and (D) Crystalzyme 100XL.
Cellulase 17600L, Crystalzyme Cran, and Crystalzyme 100XL shown higher activity at 50°C, while to Crystalzyme PML-MX was at 45°C. These conditions are within the limit considered by [34] to maintain the neem extracts without the loss of azadirachtin. However, it is necessary to evaluate these conditions on the release of azadirachtin, since it is unknown whether the highest hydrolysis of the neem seed ensures maximum concentration of azadirachtin.
Results of the enzyme concentration effect over hydrolysis of neem seeds could be observed in Figure 3. Reducing sugar release was used as indirect measure of neem seed hydrolysis.
Optimum filter paper unit (FPU) of the food-grade enzyme preparations for hydrolysis of neem seeds. (A) Crystalzyme PML-MX, (B) Cellulase 17600, (C) Crystalzyme Cran, and (D) Crystalzyme 100XL.
Reactions catalyzed by Crystalzyme PML-MX and Cellulase 17600 L presented the highest hydrolysis of the neem seed, probably due to the presence of cellulases in its composition. A total of 1.8072 (± 0.0021), 2.0635 (± 0.0689), 2.0493 (± 0.0521), and 2.0742 (± 0.0283) g L−1 reducing sugars were released when 0.5 (3.48 FPU), 1 (6.96 FPU), 2 (13.92 FPU), and 4 mL (27.84 FPU) of Crystalzyme PML-MX were used. On another hand, 1.8034 (± 0.0387), 2.0809 (± 0.0023), 1.9921 (± 0.0456), and 1.9383 (± 0.0781) g L−1 reducing sugars were released when 0.5 (2.80 FPU), 1 (5.60 FPU), 2 (11.2 FPU), and 4 mL (22.4 FPU) of Cellulase 17600 were used. These results suggest that an increasing of enzyme concentration in the reaction not necessarily imply higher hydrolysis of the neem seed. Therefore, it is advisable to use the lowest concentration in which the same results were obtained for each enzyme preparation. Crystalzyme Cran and Crystalzyme 100XL showed less hydrolysis of the neem seed after 18 h. Crystalzyme Cran released 1.0022 (± 0.0576) g L−1 with the highest concentration of enzyme used (4 mL, 9.52 FPU); however, these results were not statistically different from those found when using 1 (2.38 FPU) and 2 mL (4.76 FPU), since the equilibrium concentration of reducing sugars was 0.9323 (± 0.0786) and 0.9859 (± 0.0341), respectively. On the other hand, the samples hydrolyzed with Crystalzyme 100XL had a maximum reducing sugar release when 4 mL of enzyme (21.2 FPU) was used.
In order to determine the optimum enzyme concentration to neem seed hydrolysis, equilibrium concentration and release rate of the reducing sugars were evaluated. The analysis of the results showed that reducing sugar release rate was not statistically different when 1, 2 or 4 mL for both Crystalzyme PML-MX and Cellulase 17600 L were used. When Crystalzyme Cran was evaluated, the higher rates of release of reducing sugars were obtained with 2 and 4 mL. In contrast, the highest reduced sugar release rate for Crystalzyme 100XL was presented when 1 mL (5.30 FPU) of this enzyme was added. Therefore, based on the analysis of results, it was considered that the units of activity necessary to hydrolyze the neem seed are 6.96, 5.60, 4.76, and 5.3 FPU mL−1 for Crystalzyme PML-MX, Cellulase 17600 L, Crystalzyme Cran, and Crystalzyme 100XL, respectively.
After optimum conditions of pH, temperature and enzyme concentration were determined, and the kinetics of azadirachtin released was carried out but using fresh seed instead of dehydrated seed to minimize the risk of losses due to the dehydration process.
Evaluation of the azadirachtin release kinetics was conducted with 100 g of neem seed homogenized and adjusted at 1:10 (w v−1) ratio with phosphate buffer. Enzymatic hydrolysis was conducted under optimal conditions of each enzyme preparation, and azadirachtin were quantified at 0, 2, 4, 6, 12, 18, and 24 h by the HPLC technique, and the results were presented on base of the quantity of neem seed (dry base) used for the neem seed extract elaboration (Figure 4). The highest azadirachtin concentrations obtained in this study were 2.55 g kg−1 (2550 ppm) and 2.35 g kg−1 (2350 ppm) neem seed when Cellulase 17600 L and Crystalzyme PML-MX enzyme preparations were used, respectively. Both of this enzyme preparation include cellulases and pectinases and were statistically different (p < 0.05) from the enzymatic preparations, Crystalzyme Cran (pectinases) and Crystalzyme 100XL (pectinases and arabinases).
Azadirachtin release kinetics from neem seeds extracted with food-grade enzyme preparations.
Azadirachtin concentrations found in this study were higher than those reported by other authors when conventional methods such as extrusion, extraction with solvents (hexane, methanol), and aqueous extraction which reported concentrations of 1080, 565, 400, 150 ppm, respectively [33] and were similar to the obtaining by cold methanol extrusion [32]. However, the concentration obtained is lower than those reported with nonconventional technologies such as extraction with pressurized solvents, which reported concentrations of up to 9510 ppm of azadirachtin [34].
One of the advantages of the use of organic solvents in the extraction process is to improve the solubility of nonpolar components which increase their extraction from vegetable matrices. However, in the present study, only phosphate buffer was used, which could explain the differences of azadirachtin extraction with the pressurized solvent method [34]. Also, in [43], it was reported that in the tropical regions, there are lower concentrations of azadirachtin after extraction processes due to high temperatures, moisture, and storage conditions that could encourage the azadirachtin degradation.
Although the highest concentrations of azadirachtin were obtained with the enzymatic preparations, Cellulase 17600L and Crystalzyme PML-MX, also Crystalzyme Cran and Crystalzyme 100XL could be used to elaborate neem extracts because of their azadirachtin concentrations of 1540 and 1690 ppm, respectively, are similar to those contained in commercial acaricidal products elaborated based on neem, but with the use of solvents [32]. This result indicates an advantage and a possible solution to the indiscriminate use of synthetic pesticides and the organic solvents employed in several extractions of active biomolecules. On the other hand, after 18 h of enzymatic hydrolysis, there are no changes on azadirachtin release, which means it is time required to carry out the obtaining of neem seed extracts. In addition, the conditions identified in this study as necessary to neem seed hydrolysis are not extreme or aggressive and could be easily scalable.
The present study had the objective of generating an alternative solution to the multiple problems generated by the indiscriminate use of synthetic pesticides. The use of enzymes in the production of metabolites with biological interest has been widely studied in the last decade, due to their specificity and their effect on the cellulose substrate hydrolysis, minimizing the times of obtaining and the necessary costs for their implementation. In addition, its application allows reducing the use of solvents, normally employed in the extraction of neem oil. Besides, the conditions required for neem seed hydrolysis under optimal activity of the enzyme preparations can be easily standardized and scaled for its industrial production. In addition, it was found no affectations on azadirachtin concentration when temperatures of 45 and 50°C were used during enzymatic hydrolysis coinciding with that reported in [34]. However, more studies are needed to determine the stability of azadirachtin under different temperatures and pH conditions during storage.
Although the enzyme-assisted extraction allows the obtaining of neem extracts with higher azadirachtin concentrations than those obtained with conventional methods such as extrusion, cold extrusion, water maceration, and percolation with hexane [32, 33] and in similar concentrations to those obtained by extrusion with cold methanol [32], the amount of neem seeds used in this method was lower, and therefore, the yield was higher than obtained with all these methods. In next studies, components identified as repellents and/or acaricides such as salannin and nimbin should be analyzed. In addition, it is necessary to carry on shortterm and long-term in vitro and in vivo analysis of enzymatic neem extracts on pest with public health impact, defining the vehicle of application and the lethal concentrations for its implementation.
This study was funded by the Dirección General de Educación Superior Universitaria (DGESU/SEP) (Project DSA/103.5/14/7147).
All authors who participate in the elaboration of this manuscript have no conflict of interest to declare.
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