\r\n\t \r\n\tThis book is intended to discuss and address the issues and challenges of anomaly detection. The book is focused on, but not limited to chapters that address different aspects of anomaly detection consisting of novel strategies and development of services over different computing models. Moreover, the book intends to investigate how anomaly detection is impacted by cutting edge innovations.
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Venkata Krishna is currently a Professor of Computer Science and a Director at Sri Padmavati Mahila University, Tirupati, India. He received his B. Tech in Electronics and Communication Engineering from Sri Venkateswara University, Tirupathi, India, M. Tech in Computer Science & Engineering from REC, Calicut, India, and he received his Ph.D. from VIT University, Vellore, India. Dr. Krishna has several years of experience working in academia, research, teaching, consultancy, academic administration and project management roles. His current research interests include Mobile and wireless systems, cross-layer wireless network design, QoS, and Cloud Computing. He was the recipient of several academic and research awards such as the Cognizant Best Faculty Award for the year 2009-2010 and the VIT Most Active Researcher Award for the year 2009-2010. 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1. Introduction
Modern societies face many challenges, including growing populations, increased demands for food, clothing, housing, consumer goods, and the raw materials required to produce all of these. Additionally, there is a growing need for energy, which is most easily met by use of fossil fuels (e.g., coal, natural gas, petroleum). For example, in 2008, the overall U.S. demand for energy was 99.3 x 1015 Btu (1.05 x 1014 MJ); 84% of this was supplied by fossil sources. Transportation fuels accounted for 28% of all energy consumed during this time, and nearly 97% of this came from fossil sources. Domestic production of crude oil was 4.96 million barrels per day, whereas imports were 9.76 million barrels per day (nearly 2/3 of the total U.S. demand) (U.S. EIA, 2011). Many argue that this scenario is not sustainable in the long term, and other alternatives are needed.
Biofuels, which are renewable sources of energy, can help meet some of these increasing needs. They can technically be produced from a variety of materials which contain either carbohydrates or lipids, including cereal grains (such as corn, barley, and wheat), oilseeds (such as soybean, canola, and flax), legumes (such as alfalfa), perennial grasses (such as switchgrass, miscanthus, prairie cord grass, and others), agricultural residues (such as corn stover and wheat stems), algae, food processing wastes, and other biological materials. Indeed, the lignocellulosic ethanol industry is poised to consume large quantities of biomass in the future (Agrawal et al., 2007; Alexander and Hurt, 2007; Cassman, 2007; Cassman et al., 2006; Cassman and Liska, 2007; Dale, 2007; De La Torre Ugarte et al., 2000; Dewulf et al., 2005; Lynd and Wang, 2004). At this point in time, however, the most heavily used feedstock for biofuel production in the U.S. is corn grain. Industrial-scale alcohol production from corn starch is readily accomplished, and at a lower cost (generally between $1/gallon and $1.4/gallon), compared to other available biomass substrates in the U.S. The most commonly used process for the production of fuel ethanol from corn is the dry grind process, the primary coproduct of which is distillers dried grains with solubles (DDGS) (Figure 1), which will be discussed subsequently.
Corn-based ethanol has been used as a liquid transportation fuel for more than 150 years, although up until recent times the industry has been quite small. The modern corn-based fuel ethanol industry, however, has reached a scale which can augment the nation’s supply of transportation fuels. In 2008, for example, ethanol displaced more than 321 million barrels of oil (Urbanchuk, 2009), which accounted for nearly 5% of all oil imports. Only recently has this industry become truly visible to the average citizen. This has been due, in part, to the growing demand for transportation fuels, escalating prices at the fuel pump, positive economic effects throughout rural America, as well as questions and controversies surrounding the production and use of corn ethanol.
Figure 1.
Corn-based distillers dried grains with solubles (DDGS), which is currently available from most U.S. fuel ethanol plants.
To help meet the increasing demand for transportation fuels, the number of ethanol plants has been rapidly increasing in recent years, as has the quantity of fuel ethanol produced (Figure 2).
Figure 2.
U.S. fuel ethanol (L) and DDGS (t) production over time; RFS denotes levels mandated by the Renewable Fuel Standard. Inset shows number of U.S. ethanol plants over time (adapted from RFA, 2009a, 2009b, 2011)
In 2005, 87 manufacturing plants in the U.S. had an aggregate production capacity of 13.46 billion L/y (3.56 billion gal/y). At the beginning of 2011, however, that number had risen to 204 plants with a production capacity of nearly 51.1 billion L/y (13.5 billion gal/y), which is an increase of nearly 380% in six years (RFA, 2011). Most new ethanol plants have been dry-grind facilities (Figure 3), which will be discussed subsequently. And, over the next several years, the
Figure 3.
U.S. dry grind corn-to-ethanol manufacturing plants. A. 450 x 106 L/y plant. B. 80 x 106 L/y plant.
Renewable Fuel Standard (RFS) mandates the use of 15 billion gal/y (56.8 billion L/y) of renewable biofuels (i.e., which will primarily be corn-based ethanol) (RFA, 2009a), although the RFS does mandate the growing use of advanced and cellulosic biofuels as well. Because the industry is dynamic and still evolving, these current production numbers will surely be outdated by the time this book is published. As production volume increases, the processing residues (known collectively as “distillers grains” –will increase in tandem (as shown in Figure 2). It is anticipated that over 40 million metric tonnes (t) of distillers grains (both wet and dry) will eventually be produced by the U.S. fuel ethanol industry as production reaches equilibrium due to the RFS.
It is true that as the industry has grown, the concomitant consumption of corn has grown as well (Figure 4). Since 2008, for example, over 30% of the U.S. corn crop has been used to produce ethanol. When examining these numbers, however, it is important to be aware of several key points: exports have been relatively constant over time, there has been a slight decline in the corn used for animal feed, and the overall quantity of corn which is produced by U.S. farmers has been substantially increasing over time. Thus, it appears that the corn which is used to produce ethanol is actually arising mostly from the growing corn supply. It is also important to note that the corn which is redirected away from animal feed is actually being replaced by DDGS and other ethanol coproducts in these animal feeds. Thus coproducts (especially DDGDS) are key to the sustainability of both the ethanol and livestock industries. In other words, fuel, feed, and food needs can be simultaneously met.
Figure 4.
Historic U.S. corn production (bu) and major categories of use (adapted from ERS, 2011).
2. Objectives
The goals of this chapter are three-fold: 1) to briefly discuss U.S. fuel ethanol and coproduct manufacturing processes; 2) to explain the importance of coproducts to the fuel ethanol and livestock industries; and 3) to describe how coproduct quality is improving and potential uses are expanding as the ethanol industry continues to evolve.
3. Manufacturing processes
Corn can be converted into fuel ethanol by three commercial processes: wet milling, dry milling, and dry grind processing. Over the last decade, many new fuel ethanol plants have been built (Figure 2), and considerable innovations have occurred throughout the industry vis-à-vis production processes used and final products produced, as well as raw materials, water, and energy consumption. Many of these innovations have arisen with the advent of dry grind processing. Due to many advantages, including lower capital and operating costs (including energy inputs), most new ethanol plants are dry grind facilities as opposed to the older style mills. For example, in 2002, 50% of U.S. ethanol plants were dry grind; in 2004 that number had risen to 67%; in 2006 dry grind plants constituted 79% of all facilities; and in 2009 the fraction had grown to over 80% (RFA, 2009a).
The dry grind process (Figure 5) entails several key steps, including grain receiving, distribution, storage, cleaning, grinding, cooking, liquefaction, saccharification, fermentation, distillation, ethanol storage and loadout, centrifugation, coproduct drying, coproduct storage and loadout. Additional systems that play key roles include energy / heat recovery, waste management, grain aeration, CO2 scrubbing and extraction, dust control, facility sanitation, instrumentation and controls, and sampling and inspection. Figure 5 depicts how all of these pieces fit together in a commercial plant.
Grinding, cooking, and liquefying release and convert the corn starch into glucose, which is consumed during the fermentation process by yeast (Sacchharomyces cerevisiae). After fermentation, the ethanol is separated from the water and nonfermentable residues (which consist of corn kernel proteins, fibers, oils, and minerals) by distillation. Downstream dewatering, separation, evaporation, mixing, and drying are then used to remove water from the solid residues and to produce a variety of coproduct streams (known collectively as distillers grains): wet or dry, with or without the addition of condensed solubles (CDS). Distillers dried grains with solubles (known as DDGS), is the most popular, and is often dried to approximately 10% moisture content (or even less at some plants), to ensure an extended shelf life and good flowability, and then sold to local livestock producers or shipped by truck or rail to various destinations throughout the nation. DDGS is increasingly being exported to overseas markets as well. Distillers wet grains (or DWG) has been gaining popularity with livestock producers near ethanol plants in recent years; in fact, it has been estimated that, nationwide, more than 25% of distillers grains sales are now DWG. But, because the moisture contents are generally greater than 50 to 60%, their shelf life is very limited, especially in summer months, and shipping large quantities of water is expensive. DDGS is still the most prevalent type of distillers grain in the marketplace.
Dry grind ethanol manufacturing results in three main products: ethanol, the primary end product; residual nonfermentable corn kernel components, which are sold as distillers grains; and carbon dioxide. A common rule of thumb is that for each 1 kg of corn processed, approximately 1/3 kg of each of the constituent streams will be produced. Another rule of thumb states that each bushel of corn (~ 56 lb; 25.4 kg) will yield up to 2.9 gal (11.0 L) of ethanol, approximately 18 lb (8.2 kg) of distillers grains, and nearly 18 lb (8.2 kg) of carbon dioxide. Of course, these will vary to some degree over time due to production practices, equipment settings, residence times, concentrations, maintenance schedules, equipment conditions, environmental conditions, the composition and quality of the raw corn itself, the location where the corn was grown, as well as the growing season that produced the corn.
During fermentation, carbon dioxide arises from the metabolic conversion of sugars into ethanol by the yeast. This byproduct stream can be captured and sold to compressed gas markets, such as beverage or dry ice manufacturers. Often, however, it is released to the atmosphere because location and/or logistics make the sales and marketing of this gas economically unfeasible. In the future, however, the release of carbon dioxide may eventually be impacted by greenhouse gas emission constraints and regulations.
Figure 5.
Flow chart of typical corn dry grind fuel ethanol and coproducts processing.
DDGS from most modern U.S. fuel ethanol plants typically contains about 30% protein, 10% fat, at least 40% neutral detergent fiber, and up to 12% starch (Rosentrater and Muthukumarappan, 2006). Composition, however, can vary between plants and even within a single plant over time, due to a number of factors. For example, Table 1 summarizes composition of DDGS samples collected from five ethanol plants in South Dakota. On a dry basis, crude protein levels ranged from 28.3 to 31.8%; crude lipid varied between 9.4 and 11.0%; ash ranged from 4.1 to 13.3%. In terms of within-plant variability, the crude protein, crude lipid, and starch content all exhibited relatively low variation, whereas neutral detergent fiber (NDF), acid detergent fiber (ADF), and ash all had substantially higher variability.
Plant
Protein
Lipid
NDF
ADF
Starch
Ash
1
28.33b (1.25)
10.76a (1.00)
31.84b (4.02)
15.56a (2.29)
11.82a (1.20)
13.27a (3.10)
2
30.65a (1.20)
9.75a (1.05)
39.90a (3.95)
15.21a (3.95)
9.81a (1.52)
12.84a (2.56)
3
28.70a (1.32)
10.98a (0.95)
38.46a (4.01)
17.89a (4.01)
11.59a (1.42)
11.52a (3.05)
4
30.65a (1.23)
9.40b (0.16)
36.73a (1.07)
15.28a (0.49)
9.05b (0.33)
4.13b (0.21)
5
31.78a (0.63)
9.50b (0.41)
38.88a (0.86)
17.24a (1.12)
10.05a (0.65)
4.48b (0.22)
Table 1.
Composition (% db) of DDGS from five ethanol plants in South Dakota (± 1 standard deviation in parentheses). Statistically significant differences among plants for a given nutrient are denoted by differing letters, α=0.05, LSD (adapted from Bhadra et al., 2009).
Furthermore, DDGS from 49 plants from 12 states were analyzed for proximate composition (Table 2) and amino acid profiles (Table 3) (UMN, 2011). Dry matter content varied from 86.2% to 92.4%, while protein varied from 27.3% to 33%. Crude fat content displayed even higher variability, and ranged from 3.5% to 13.5%; crude fiber ranged from 5.37% to 10.58%; and ash content varied from 2.97% to 9.84%. On average, geographic trends were not readily apparent for any of the nutrient components. In terms of amino acids, lysine ranged from 0.61% to 1.19%, but again, no geographic trends were apparent.
Some plants are beginning to implement various fractionation processes (either pre-fermentation or post-fermentation) in order to produce multiple product streams (RFA, 2009a). These new processes can lead to additional differences in DDGS nutrient levels. For example, various techniques for dry fractionation and wet fractionation have been developed to concentrate protein, fiber, and oil components from the endosperm (which contains the starch). This allows a highly-concentrated starch substrate to be introduced to the fermentation process, and it allows the other components to be used for human food applications. Singh and Johnston (2009) have provided an extensive discussion regarding various pre-fermentation fractionation approaches. On the other hand, post-fermentation fractionation techniques have also been examined. For example, Srinivasan et al. (2005) used a combination of (air classification and sieving to separate fiber particles from DDGS. Processes have also been developed to remove corn oil from thin stillage and CDS; although the resulting corn oil fractions cannot be used as food-grade oil, they can readily be converted into biodiesel. All of these approaches, if implemented commercially, will alter the composition of the resulting DDGS.
Table 2.
Composition (% db) of DDGS samples from 49 ethanol plants from 12 states (adapted from UMN, 2011).
Table 3.
Amino acid profiles (% db) of DDGS samples from 49 ethanol plants from 12 states (adapted from UMN, 2011).
The U.S. ethanol industry’s primary market for distillers grains has historically been as a commodity livestock feed. Most often this has been in the form of DDGS, and to a lesser degree in the form of DWG; the other coproducts are sold in much lower quantities than either DDGS or DWG and some are not always produced either). Feeding ethanol coproducts to animals is a practical method of utilizing these materials because they contain high nutrient levels, and they are digestible (to varying degrees) by most livestock. And, use of DDGS in animal feeds (instead of corn grain) helps to offset the corn which has been redirected to ethanol production. Over 80% of all distillers grains is used in beef and dairy diets; due to their ability to utilize high levels of fiber, ruminant animals have become the dominant consumers of DDGS. But, as livestock producers and animal nutritionists increase their knowledge, through research and experience, the swine and poultry markets are also increasing their consumption as well (UMN, 2011). Over the years, numerous research studies have been conducted on coproduct use in livestock diets, for both ruminant and monogastric feeds. Table 4 lists some of this research. Depending on the diet composition used, all livestock species have been shown to thrive at 10% DDGS inclusion, and most can tolerate levels up to 20% (or even more).
Species
Citation
Species
Citation
Beef
Dairy
Loy et al., 2007
Kleinschmit et al., 2007
MacDonald et al., 2007
Anderson et al., 2006
Martin et al., 2007
Kleinschmit et al., 2006
Roeber et al., 2005
Leonardi et al., 2005
Al-Suwaiegh et al., 2002
Birkelo et al., 2004
Peter et al., 2000
McKendrick et al., 2003
Lodge et al., 1997a
Al-Suwaiegh et al., 2002
Lodge et al., 1997b
Liu et al., 2000
Fron et al., 1996
Huang et al., 1999
Klopfenstein, 1996
Schingoethe et al., 1999
Ham et al., 1994
Batajoo and Shaver, 1998
Larson et al., 1993
Nichols et al., 1998
Donaldson et al., 1991
Clark and Armentano, 1997
McCann et al., 1991
DePeters et al., 1997
O’Mara et al., 1997
Zhu et al., 1997
Arosemena et al., 1995
Murphy et al., 1995
Powers et al., 1995
Ham et al., 1994
Clark and Armentano, 1993
Swine
Poultry
Stein and Shurson, 2009
Waldroup et al., 2007
Pedersen et al., 2007
Wang et al., 2007a
Widmer et al., 2007
Wang et al., 2007b
Fastinger et al., 2007
Wang et al., 2007c
Stein et al., 2006
Batal and Dale, 2006
Whitney et al., 2006a
Fastinger et al., 2006
Whitney et al., 2006b
Martinez-Amezcua et al., 2006
Whitney et al., 2006c
Noll, 2006
Whitney et al., 2006d
Lumpkins and Batal, 2005
Nyachoti et al., 2005
Lumpkins et al., 2005
Whitney and Shurson, 2004
Roberson et al., 2005
Gralapp et al., 2002
Biggs et al., 2004
Spiehs et al., 2002
Lumpkins et al., 2004
Nicolai et al., 1999
Martinez Amezcua et al., 2004
Cromwell et al., 1993
Batal and Dale, 2003
Roberson, 2003
Cromwell et al., 1993
Table 4.
Table 4. Summary of livestock research on fuel ethanol coproducts.
DDGS use in livestock diets has continued to increase over the years. Predictions of peak potential for DDGS use in domestic U.S. beef, dairy, swine, and poultry markets have estimated that between 40 and 60 million t could be used in the U.S. each year, depending upon inclusion rates for each species (Staff, 2005; Cooper, 2006; U.S. Grains Council, 2007). Globally, the need for protein-based animal feeds continues to grow. Of the 23 million t of DDGS produced in 2008 (RFA, 2009b), 4.5 million t were exported to international markets (FAS, 2009); this accounted for nearly 20% of the U.S. DDGS production that year (Figure 6). And the potential for global exports is projected to increase for the foreseeable future (U.S. Grains Council, 2007).
Figure 6.
A. U.S. DDGS exports in 2008. B. Countries who imported DDGS in 2008 (adapted from Hoffman and Baker, 2010).
Not only are coproducts important to the livestock industry as feed ingredients, but they are also essential to the sustainability of the fuel ethanol industry itself. In fact, the sale of distillers grains (all types – dry and wet) contributes substantially to the economic viability of each ethanol plant (sales can generally contribute between 10 and 20% of a plant’s total revenue stream (Figure 7), but at times it can be as high as 40%), depending upon the market conditions for corn, ethanol, and distillers grains. This is the reason why these process residues are referred to as “coproducts”, instead of “byproducts” or “waste products”; they truly are products in their own right along with the fuel.
Figure 7.
Some relative comparisons of the value of DDGS and fuel ethanol to ethanol plant profits (adapted from DTN, 2011).
Figure 8.
DDGS sales price over time (monthly averages) (adapted from ERS, 2011).
So the sales price of DDGS is important to ethanol manufacturers and livestock producers alike. Over the last three decades, the price for DDGS has ranged from approximately $50.71/t up to $209.44/t (Figure 8). DDGS and corn prices have historically paralleled each other very closely (Figure 9). This relationship has been quite strong over the last several
Figure 9.
A. Some comparisons of DDGS, soybean meal (SBM), and corn sales prices. B. Relative price comparisons. C. Cost comparisons on a per unit protein basis (adapted from DTN, 2011).
years. This is not surprising, as DDGS is most often used to replace corn in livestock diet formulations. DDGS has increasingly been used as a replacement for soybean meal as well, primarily as a source of protein. Even so, DDGS has historically been sold at a discounted price vis-à-vis both corn and soybean meal. This has been true on a volumetric unit basis, as well as per unit protein basis (Figure 9).
5. Coproduct evolution
The ethanol industry is dynamic and has been evolving over the years in order to overcome various challenges associated with both fuel and coproduct processing and use (Rosentrater, 2007). A modern dry grind ethanol plant is considerably different from the inefficient, input-intensive Gasohol plants of the 1970s. New developments and technological innovations, to name but a few, include more effective enzymes, higher starch conversions, better fermentations, cold cook technologies, improved drying systems, decreased energy consumption throughout the plant, increased water efficiency and recycling, and decreased emissions. Energy and mass balances are becoming more efficient over time. Many of these improvements can be attributed to the design and operation of the equipment used in modern ethanol plants. A large part is also due to computer-based instrumentation and control systems.
Many formal and informal studies have been devoted to adjusting existing processes in order to improve and optimize the quality of the coproducts which are produced. Ethanol companies have recognized the need to produce more consistent, higher quality DDGS which will better serve the needs of livestock producers. The sale of DDGS and the other coproducts has been one key to the industry’s success so far, and will continue to be important to the long-term sustainability of the industry. Although the majority of DDGS is currently consumed by beef and dairy cattle, use in monogastric diets, especially swine and poultry, continues to increase. And use in non-traditional species, such as fish, horses, and pets has been increasing as well.
Additionally, there has been considerable interest in developing improved mechanisms for delivering and feeding DDGS to livestock vis-à-vis pelleting/densification (Figure 10). This is a processing operation that could result in significantly better storage and handling characteristics of the DDGS, and it would drastically lower the cost of rail transportation and logistics (due to increased bulk density and better flowability) (Figure 11). Pelleting could also broaden the use of DDGS domestically (e.g., improved ability to use DDGS for rangeland beef cattle feeding and dairy cattle feeding) as well as globally (e.g., increased bulk density would result in considerable freight savings in bulk vessels and containers).
There are also many new developments underway in terms of evolving coproducts. These will ultimately result in more value streams from the corn kernel (i.e., upstream fractionation) as well as the resulting distillers grains (i.e., downstream fractionation) (Figure 12). Effective fractionation can result in the separation of high-, mid-, and low-value components. Many plants have begun adding capabilities to concentrate nutrient streams such as oil, protein, and fiber into specific fractions, which can then be used for targeted markets and specific uses. These new processes are resulting in new types of distillers grains (Figure 13).
Figure 10.
Pelleting is a unit operation that can improve the utility of DDGS, because it improves storage and handling characteristics, and allows more effective use in dairy cattle feeding and range land settings for beef cattle.
Figure 11.
By pelleting, empty space in rail cars is minimized during shipping. Techno-economic analysis of the resulting slack (i.e., wasted space) costs and costs of pelleting for each rail car due to differing DDGS sales prices and pelleting costs indicates the proportion of DDGS which needs to be pelleted in order to achieve breakeven for this process (adapted from Rosentrater and Kongar, 2009).
Figure 12.
Fractionation of DDGS into high-, mid-, and low-value components offers the opportunity for new value streams.
Figure 13.
Examples of traditional, unmodified DDGS and some fractionated products (e.g., high-protein and low-fat DDGS) which are becoming commercially available in the marketplace.
For example, if the lipids are removed from the DDGS (Figure 14), they can readily be converted into biodiesel, although they cannot be used for food grade corn oil, because they are too degraded structurally. Another example is concentrated proteins, which can be used for high-value animal feeds (such as aquaculture or pet foods), or other feed applications which require high protein levels. Additionally, DDGS proteins can be used in human foods (Figure 15). Furthermore, other components, such as amino acids, organic acids, or even nutraceutical compounds (such as phytosterols and tycopherols) can be harvested and used in high-value applications.
Mid-value components, such as fiber, can be used as biofillers for plastic composites (Figure 16), as feedstocks for the production of bioenergy (e.g., heat and electricity at the ethanol plant via thermochemical conversion) (Figure 17), or, after pretreatment to break down the lignocellulosic structures, as substrates for the further production of ethanol or other biofuels.
In terms of potential uses for the low-value components, hopefully mechanisms will be developed to alter their structures and render them useful, so that they will not have to be landfilled. Fertilizers are necessary in order to sustainably maintain the flow of corn grain into the ethanol plant, so land application may be an appropriate venue for the low value components.
As these process modifications are developed, validated, and commercially implemented, improvements in the generated coproducts will be realized and unique materials will be produced. Of course, these new products will require extensive investigation in order to determine how to optimally use them and to quantify their value propositions in the marketplace.
Figure 14.
Corn oil which has been extracted from DDGS can be used to manufacture biodiesel.
Figure 15.
As a partial substitute for flour, high-value DDGS protein can be used to improve the nutrition of various baked foods such as (A) bread, (B) flat bread, and (C) snack foods, by increasing protein levels and decreasing starch content.
Figure 16.
Mid-value or low-value fractions from DDGS (such as fiber) have been shown to be an effective filler in plastics, replacing petroleum additives and increasing biodegradability. Scale bar indicates mm.
Figure 17.
Mid-value or low-value fractions from DDGS (such as fiber) can be thermochemically converted into biochar, which can subsequently be used to produce energy, fertilizer, or as a precursor to other bio-based materials.
6. Conclusion
The fuel ethanol industry has been rapidly expanding in recent years in response to government mandates, but also due to increased demand for alternative fuels. This has become especially true as the price of gasoline has escalated and fluctuated so drastically, and the consumer has begun to perceive fuel prices as problematic. Corn-based ethanol is not the entire solution to our transportation fuel needs. But it is clearly a key component to the overall goal of energy independence. Corn ethanol will continue to play a leading role in the emerging bioeconomy, as it has proven the effectiveness of industrial-scale biotechnology and bioprocessing for the production of fuel. And it has set the stage for advanced biorefineries and manufacturing techniques that will produce the next several generations of advanced biofuels. As the biofuel industry continues to evolve, coproduct materials (which ultimately may take a variety of forms, from a variety of biomass substrates) will remain a cornerstone to resource and economic sustainability. A promising mechanism to achieve sustainability will entail integrated systems (Figure 18), where material and energy streams cycle and recycle (i.e., upstream outputs become downstream inputs) between various components of a biorefinery, animal feeding operation, energy (i.e., heat, electricity, steam, etc.) production system, feedstock production system, and other systems. By integrating these various components, a diversified portfolio will not only produce fuel, but also fertilizer, feed, food, industrial products, energy, and most importantly, will be self-sustaining.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/17480.pdf",chapterXML:"https://mts.intechopen.com/source/xml/17480.xml",downloadPdfUrl:"/chapter/pdf-download/17480",previewPdfUrl:"/chapter/pdf-preview/17480",totalDownloads:4377,totalViews:1058,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,dateSubmitted:"October 19th 2010",dateReviewed:"March 24th 2011",datePrePublished:null,datePublished:"August 1st 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/17480",risUrl:"/chapter/ris/17480",book:{slug:"biofuel-s-engineering-process-technology"},signatures:"Kurt A. Rosentrater",authors:[{id:"27500",title:"Dr.",name:"Kurt",middleName:"A.",surname:"Rosentrater",fullName:"Kurt Rosentrater",slug:"kurt-rosentrater",email:"karosent@iastate.edu",position:null,institution:{name:"Iowa State University",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Objectives",level:"1"},{id:"sec_3",title:"3. Manufacturing processes",level:"1"},{id:"sec_4",title:"4. Importance of coproducts",level:"1"},{id:"sec_5",title:"5. Coproduct evolution",level:"1"},{id:"sec_6",title:"6. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'AgrawalR.SinghN. R.RibeiroF. H.DelgassW. N.\n\t\t\t\t\t2007\n\t\t\t\t\tSustainable fuel for the transportation sector. Proceedings of the National Academy of Sciences\n\t\t\t\t\t104\n\t\t\t\t\t12\n\t\t\t\t\t48284833 .'},{id:"B2",body:'AlexanderC.HurtC.\n\t\t\t\t\t2007\n\t\t\t\t\tBiofuels and their impact on food prices. Bioenergy ID-346 -W. Department of Agricultural Economics, Purdue University: West Lafayette, IN.'},{id:"B3",body:'Al-SuwaieghS.FanningK. C.GrantR. J.MiltonC. T.KlopfensteinT. J.\n\t\t\t\t\t2002\n\t\t\t\t\tUtilization of distillers grains from the fermentation of sorghum or corn in diets for finishing beef and lactating dairy cattle. J. Anim. 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W.WangZ.CotoC.CerrateS.YanF.\n\t\t\t\t\t2007\n\t\t\t\t\tDevelopment of a standardized nutrient matrix for corn distillers dried grains with solubles. Internatl. Journal of Poultry Sci. 6\n\t\t\t\t\t7\n\t\t\t\t\t478483 .\n\t\t\t'},{id:"B89",body:'WangZ.CerrateS.CotoC.YanF.WaldroupP. W.\n\t\t\t\t\t2007a\n\t\t\t\t\tEffect of rapid and multiple changes in level of distillers dried grain with solubles (DDGS) in broiler diets on performance and carcass characteristics. International Journal of Poultry Sci. 6\n\t\t\t\t\t10\n\t\t\t\t\t725731 .'},{id:"B90",body:'WangZ.CerrateS.CotoC.YanF.WaldroupP. W.\n\t\t\t\t\t2007b\n\t\t\t\t\tUse of constant or increasing levels of distillers dried grains with solubles (DDGS) in broiler diets. International Journal of Poultry Sci. 6\n\t\t\t\t\t7\n\t\t\t\t\t501507 .'},{id:"B91",body:'WangZ.CerrateS.CotoC.YanF.WaldroupP. W.\n\t\t\t\t\t2007c\n\t\t\t\t\tUtilization of distillers dried grains with solubles (DDGS) in broiler diets using a standardized nutrient matrix. International Journal of Poultry Sci. 6\n\t\t\t\t\t7\n\t\t\t\t\t470477 .\n\t\t\t'},{id:"B92",body:'WeigelJ. C.LoyD.KilmerL.\n\t\t\t\t\t1997\n\t\t\t\t\tFeed Co-Products of the Dry Corn Milling Process. Iowa State University, Iowa Corn Promotion Board, Iowa Department of Agriculture, Renewable Fuels Association, National Corn Growers Association. Available online: www.iowacorn.org/ethanol/ethanol_17.html.'},{id:"B93",body:'WhitneyM. H.ShursonG. C.\n\t\t\t\t\t2004\n\t\t\t\t\tGrowth performance of nursery pigs fed diets containing increasing levels of corn distiller’s dried grains with solubles originating from a modern Midwestern ethanol plant. J. Anim. Sci. 82\n\t\t\t\t\t122128 .\n\t\t\t'},{id:"B94",body:'WhitneyM. H.ShursonG. C.GuedesR. C.\n\t\t\t\t\t2006a\n\t\t\t\t\tEffect of dietary inclusion of distillers dried grains with solubles on the ability of growing pigs to resist a Lawsonia intracellularis challenge. J. Anim. Sci. 84\n\t\t\t\t\t18601869 .\n\t\t\t'},{id:"B95",body:'WhitneyM. H.ShursonG. C.GuedesR. C.\n\t\t\t\t\t2006b\n\t\t\t\t\tEffect of including distillers dried grains with solubles in the diet, with or without antimicrobial regimen, on the ability of growing pigs to resist a Lawsonia intracellularis challenge. J. Anim. Sci. 84\n\t\t\t\t\t18701879 .\n\t\t\t'},{id:"B96",body:'WhitneyM. H.ShursonG. C.GuedesR. C.\n\t\t\t\t\t2006c\n\t\t\t\t\tEffect of dietary inclusion of distillers dried grains with solubles, soybean hulls, or a polyclonal antibody product on the ability of growing pigs to resist a Lawsonia intracellularis challenge. J. Anim. Sci. 84\n\t\t\t\t\t18801889 .'},{id:"B97",body:'WhitneyM. H.ShursonG. C.JohnstonL. J.WulfD. M.ShanksB. C.\n\t\t\t\t\t2006d\n\t\t\t\t\tGrowth performance and carcass characteristics of grower-finisher pigs fed high-quality corn distillers dried grain with solubles originating from a modern Midwestern ethanol plant. J. Anim. Sci. 84\n\t\t\t\t\t33563363 .'},{id:"B98",body:'WidmerM. R.Mc GinnisL. M.SteinH. H.\n\t\t\t\t\t2007 Energy, phosphorus, and amino acid digestibility of high-protein distillers dried grains and corn germ fed to growing pigs. J. Anim. Sci. 85\n\t\t\t\t\t29943003 .'},{id:"B99",body:'ZhuJ. S.StokesS. R.MurphyM. R.\n\t\t\t\t\t1997\n\t\t\t\t\tSubstitution of neutral detergent fiber from forage with neutral detergent fiber from by-products in the diets of lactating cows. J. Dairy Sci. 80\n\t\t\t\t\t29012906 .'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kurt A. Rosentrater",address:"",affiliation:'
United States Department of Agriculture, Agricultural Research Service,, U.S.A.
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Chandel, Silvio Silvério da Silva and Om V. Singh",authors:[{id:"25160",title:"Prof.",name:"Om",middleName:"V",surname:"Singh",fullName:"Om Singh",slug:"om-singh"},{id:"51540",title:"Dr.",name:"Silvio",middleName:null,surname:"Silva",fullName:"Silvio Silva",slug:"silvio-silva"},{id:"76898",title:"Dr.",name:"Anuj",middleName:null,surname:"Chandel",fullName:"Anuj Chandel",slug:"anuj-chandel"}]},{id:"20064",title:"Biofuel From Cellulosic Mass with Incentive for Feed Industry Employing Thermophilic Microbes",slug:"biofuel-from-cellulosic-mass-with-incentive-for-feed-industry-employing-thermophilic-microbes",signatures:"Javed Iqbal Qazi, Naureen Chaudhary and Saima Shahzad Mirza",authors:[{id:"25478",title:"Dr.",name:"Javed Iqbal",middleName:null,surname:"Qazi",fullName:"Javed Iqbal Qazi",slug:"javed-iqbal-qazi"},{id:"136682",title:"PhD.",name:"Naureen",middleName:null,surname:"Chaudhary",fullName:"Naureen Chaudhary",slug:"naureen-chaudhary"},{id:"136683",title:"PhD.",name:"Saima Shahzad",middleName:null,surname:"Mirza",fullName:"Saima Shahzad Mirza",slug:"saima-shahzad-mirza"}]},{id:"20065",title:"Kinetic Modelling of Dilute Acid Hydrolysis of Lignocellulosic Biomass",slug:"kinetic-modelling-of-dilute-acid-hydrolysis-of-lignocellulosic-biomass",signatures:"P. Lenihan, A. Orozco, E. O’Neill, M.N.M. Ahmad, D.W. Rooney, C. Mangwandi and G.M. Walker",authors:[{id:"27361",title:"Dr.",name:"Gavin",middleName:null,surname:"walker",fullName:"Gavin walker",slug:"gavin-walker"}]},{id:"20066",title:"Trichoderma reesei: A Fungal Enzyme Producer for Cellulosic Biofuels",slug:"trichoderma-reesei-a-fungal-enzyme-producer-for-cellulosic-biofuels",signatures:"Bernhard Seiboth, Christa Ivanova and Verena Seidl-Seiboth",authors:[{id:"26474",title:"Dr.",name:"Bernhard",middleName:null,surname:"Seiboth",fullName:"Bernhard Seiboth",slug:"bernhard-seiboth"},{id:"43389",title:"Dr.",name:"Verena",middleName:null,surname:"Seidl-Seiboth",fullName:"Verena Seidl-Seiboth",slug:"verena-seidl-seiboth"},{id:"87418",title:"MSc.",name:"Christa",middleName:null,surname:"Ivanova",fullName:"Christa Ivanova",slug:"christa-ivanova"}]},{id:"20067",title:"Application of Response Surface Methodology to Optimize Alkali Concentration, Corn Stover Particle Size, and Extruder Parameters for Maximum Sugar Recovery",slug:"application-of-response-surface-methodology-to-optimize-alkali-concentration-corn-stover-particle-si",signatures:"Chinnadurai Karunanithy and Kasiviswanathan Muthukumarappan",authors:[{id:"25176",title:"Dr.",name:"Chinnadurai",middleName:null,surname:"Karunanithy",fullName:"Chinnadurai Karunanithy",slug:"chinnadurai-karunanithy"},{id:"28898",title:"Dr.",name:"Kasiviswanathan",middleName:null,surname:"Muthukumarappan",fullName:"Kasiviswanathan Muthukumarappan",slug:"kasiviswanathan-muthukumarappan"}]},{id:"20068",title:"Innovative Biological Solutions to Challenges in Sustainable Biofuels Production",slug:"innovative-biological-solutions-to-challenges-in-sustainable-biofuels-production",signatures:"Xiaohan Yang, Ting Li, David Weston, Abhijit Karve, Jessy L. Labbe, Lee E. Gunter, Poornima Sukumar, Anne Borland, Jin-Gui Chen, Stan D. Wullschleger, Timothy J. Tschaplinski and Gerald A. Tuskan",authors:[{id:"28445",title:"Dr.",name:"Xiaohan",middleName:null,surname:"Yang",fullName:"Xiaohan Yang",slug:"xiaohan-yang"},{id:"38876",title:"Dr.",name:"Ting",middleName:null,surname:"Li",fullName:"Ting Li",slug:"ting-li"},{id:"38877",title:"Dr.",name:"David",middleName:null,surname:"Weston",fullName:"David Weston",slug:"david-weston"},{id:"38878",title:"Dr.",name:"Anne",middleName:null,surname:"Borland",fullName:"Anne Borland",slug:"anne-borland"},{id:"38879",title:"Dr.",name:"Stan",middleName:null,surname:"Wullschleger",fullName:"Stan Wullschleger",slug:"stan-wullschleger"},{id:"38880",title:"Dr.",name:"Timothy",middleName:null,surname:"Tschaplinski",fullName:"Timothy Tschaplinski",slug:"timothy-tschaplinski"},{id:"38881",title:"Dr.",name:"Gerald",middleName:null,surname:"Tuskan",fullName:"Gerald Tuskan",slug:"gerald-tuskan"},{id:"94926",title:"Dr.",name:"Abhijit",middleName:null,surname:"Karve",fullName:"Abhijit Karve",slug:"abhijit-karve"},{id:"94930",title:"Dr.",name:"Jessy",middleName:null,surname:"Labbe",fullName:"Jessy Labbe",slug:"jessy-labbe"},{id:"94931",title:"Ms.",name:"Lee",middleName:null,surname:"Gunter",fullName:"Lee Gunter",slug:"lee-gunter"},{id:"94934",title:"Dr.",name:"Jin-Gui",middleName:null,surname:"Chen",fullName:"Jin-Gui Chen",slug:"jin-gui-chen"}]},{id:"20069",title:"Microwave-Assisted Synthesis of Biofuels",slug:"microwave-assisted-synthesis-of-biofuels",signatures:"Armando T. Quitain, Shunsaku Katoh and Motonobu Goto",authors:[{id:"31504",title:"Dr.",name:"Motonobu",middleName:null,surname:"Goto",fullName:"Motonobu Goto",slug:"motonobu-goto"},{id:"37904",title:"Dr.",name:"Armando",middleName:"Tibigin",surname:"Quitain",fullName:"Armando Quitain",slug:"armando-quitain"},{id:"37905",title:"Dr.",name:"Shunsaku",middleName:null,surname:"Katoh",fullName:"Shunsaku Katoh",slug:"shunsaku-katoh"}]},{id:"20070",title:"Fertilizer Potential of Biofuel Byproducts",slug:"fertilizer-potential-of-biofuel-byproducts",signatures:"Amber Moore",authors:[{id:"24938",title:"Prof.",name:"Amber",middleName:"Dawn",surname:"Moore",fullName:"Amber Moore",slug:"amber-moore"}]},{id:"20071",title:"The Past, Present, and Future of Biofuels – Biobutanol as Promising Alternative",slug:"the-past-present-and-future-of-biofuels-biobutanol-as-promising-alternative",signatures:"Köpke Michael, Noack Steffi and Dürre Peter",authors:[{id:"37463",title:"Dr.",name:"Peter",middleName:null,surname:"Dürre",fullName:"Peter Dürre",slug:"peter-durre"},{id:"38074",title:"Dr.",name:"Steffi",middleName:null,surname:"Noack",fullName:"Steffi Noack",slug:"steffi-noack"},{id:"38075",title:"Dr.",name:"Michael",middleName:null,surname:"Köpke",fullName:"Michael Köpke",slug:"michael-kopke"}]},{id:"20072",title:"DMF - A New Biofuel Candidate",slug:"dmf-a-new-biofuel-candidate",signatures:"Guohong Tian, Ritchie Daniel and Hongming Xu",authors:[{id:"44550",title:"Prof.",name:"Hongming",middleName:null,surname:"Xu",fullName:"Hongming Xu",slug:"hongming-xu"},{id:"100409",title:"Dr.",name:"Guohong",middleName:null,surname:"Tian",fullName:"Guohong Tian",slug:"guohong-tian"},{id:"100412",title:"Mr.",name:"Ritchie",middleName:null,surname:"Daniel",fullName:"Ritchie Daniel",slug:"ritchie-daniel"}]},{id:"20073",title:"Biofuels: From Hopes to Reality",slug:"biofuels-from-hopes-to-reality",signatures:"Carioca J.O.B., Friedrich, H. and Ehrenberger, S.",authors:[{id:"82687",title:"Dr",name:"José Osvaldo",middleName:null,surname:"Beserra Carioca",fullName:"José Osvaldo Beserra Carioca",slug:"jose-osvaldo-beserra-carioca"},{id:"82693",title:"Dr",name:"Horst",middleName:null,surname:"Friedrich",fullName:"Horst Friedrich",slug:"horst-friedrich"},{id:"82694",title:"Mrs",name:"Simone",middleName:null,surname:"Ehrenberger",fullName:"Simone Ehrenberger",slug:"simone-ehrenberger"}]},{id:"20074",title:"Bioproduction of Hydrogen with the Assistance of Electrochemical Technology",slug:"bioproduction-of-hydrogen-with-the-assistance-of-electrochemical-technology",signatures:"Soundarrajan Chandrasekaran and Dachamir Hotza",authors:[{id:"25552",title:"Prof.",name:"Dachamir",middleName:null,surname:"Hotza",fullName:"Dachamir Hotza",slug:"dachamir-hotza"},{id:"44695",title:"Ph.D. Student",name:"Soundarrajan",middleName:null,surname:"Chandrasekaran",fullName:"Soundarrajan Chandrasekaran",slug:"soundarrajan-chandrasekaran"}]},{id:"20075",title:"A Genetic-Fuzzy System for Modelling of Selected Processes in Diesel Engine Fuelled by Biofuels",slug:"a-genetic-fuzzy-system-for-modelling-of-selected-processes-in-diesel-engine-fuelled-by-biofuels",signatures:"Michał Kekez and Leszek Radziszewski",authors:[{id:"38787",title:"Dr.",name:"Michał",middleName:null,surname:"Kekez",fullName:"Michał Kekez",slug:"michal-kekez"},{id:"38812",title:"Prof.",name:"Leszek",middleName:null,surname:"Radziszewski",fullName:"Leszek Radziszewski",slug:"leszek-radziszewski"}]},{id:"20076",title:"Determination of the Impact of Biogas on the Engine Oil Condition Using a Sensor Based on Corrosiveness",slug:"determination-of-the-impact-of-biogas-on-the-engine-oil-condition-using-a-sensor-based-on-corrosiven",signatures:"C. Schneidhofer, S. Sen and N. Dörr",authors:[{id:"38255",title:"MSc",name:"Christoph",middleName:null,surname:"Schneidhofer",fullName:"Christoph Schneidhofer",slug:"christoph-schneidhofer"},{id:"38280",title:"BSc.",name:"Sedat",middleName:null,surname:"Sen",fullName:"Sedat Sen",slug:"sedat-sen"},{id:"38281",title:"Dr.",name:"Nicole",middleName:null,surname:"Dörr",fullName:"Nicole Dörr",slug:"nicole-dorr"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"70956",title:"Design of Three-Term Controller Using a PIC18F452 Microcontroller",doi:"10.5772/intechopen.89815",slug:"design-of-three-term-controller-using-a-pic18f452-microcontroller",body:'
1. Introduction
The PID (Proportional Integral Differential) algorithm is the most popular feedback controller used within the process industries. It has been successfully used for over 50 years. It is a robust easily understood algorithm that can provide excellent control performance despite the varied dynamic characteristics of process plant. It is designed to generate an output that causes some corrective effort to be applied to a process so as to drive a measurable process variable towards a desired value, known as the set point. The concept is based (as shown in Figure 1) on the re-input of the system own output according to certain laws (hence the name “feedback”). It is desired for the system output to follow the set point. All feedback controllers determine their output by observing the difference, called error, between the set point and the actual process variable measurement. The PID looks at (a) the current value of the error, (b) the integral of the error over a recent time interval, and (c) the current derivative of the error signal to determine not only how much of a correction to apply, but for how long. Each of those three quantities are multiplied by a (tuning constant) and added together. Thus the PID output is a weighted sum. Depending on the application one may want a faster convergence speed or a lower overshoot. By adjusting the weighting constants, Kp, Ki, and Kd, the PID is set to give the most desired performance.
Figure 1.
Typical closed loop control system.
As a result of enormous development in microcomputer technology, analog controllers have been replaced by digital controllers either in small or large industry. It is now a common practice to implement PID controllers in its digital version, which means that they operate in discrete time domain and deal with analog signals quantized in a limited number of levels. The trend toward digital rather than analog control is mainly due to: (1) versatility where programs can be easily modified or completely changed, (2) sophistication where advanced control laws could be implemented, (3) cost effectiveness where microcontrollers are available at very low costs compared to PLCs, industrial computers, RTUs or DCS. A typical digital feedback control system is shown in Figure 2. In digital feedback systems, the controller input and output are digital (sampled) rather than continuous signals. Thus, the continuous signal from the measurement device (sensor/transmitter) is sampled and converted periodically to a digital signal by an analog-to-digital converter (ADC). A digital control algorithm is then used to calculate the controller output as a digital signal. Because most final control elements are analog devices, the digital output signal is usually converted to a corresponding analog signal by a digital-to-analog converter (DAC).
Figure 2.
Digital closed loop based on a microcontroller.
In feedback control, the objective is to reduce the error signal to zero where
et=yspt−ymtE1
where et = error signal, yspt = set point and ymt = measured value of the controlled variable. For the PID controller, the three terms (proportional, integral, and derivative) are combined to minimize the error as much as possible. The most common combination of these three terms is in parallel as shown in Figure 3. The PID equation [1, 2] is given by:
A straightforward way of deriving a digital version of the parallel form of the PID controller is to replace the integral and derivative terms by finite difference approximations,
∫0tetdt≈∑j=1kejTSE3
dedt≈ek−ek−1TSE4
where TS = the sampling time (the time between successive measurements of the controlled variable); and ek = error at the kth sampling instant for k = 1, 2, 3, …
There are two alternative forms of the digital PID control equation, the position form and the velocity form. Substituting Eqs. (3) and (4) into (2) gives the position form:
Pk=P¯+KCek+TSτI∑j=1kej+τDTSek−ek−1E5
where Pk is the controller output at the Kth sampling instant. Eq. (5) is referred to as the position form of the PID control algorithm because the actual value of the controller output is calculated [3, 4].
In the velocity form, the change in controller output is calculated. The velocity form can be derived by writing the position form of Eq. (5) for the (k − 1) sampling instant:
Pk−1=P¯+KCek−1+TSτI∑j=1k−1ej+τDTSek−1−ek−2E6
Note that the summation still begins at j = 1 because it is assumed that the process is at the desired steady state for j ≤ 0, and thus ej = 0 for j ≤ 0. Subtracting Eq. (6) from (5) gives the velocity form of the digital PID algorithm:
ΔPk=Pk−Pk−1=KCek−ek−1+TSτIek+τDTSek−2ek−1+ek−2E7
Pk=Pk−1+KCek−ek−1+TSτIek+τDTSek−2ek−1+ek−2E8
In this study, velocity form is chosen because of the following advantages:
It does not need initialization. The position form requires the initial value of the controller output P¯, which is not normally known in practice. For example, an operator keeps the control loop in the manual mode until a desired steady state operation has been reached. At this point the error is zero and the position of the control valve would correspond to the P¯ value. Therefore, if the operator would like to transfer the control from manual to automatic, he or she should enter in the position control algorithm the value of P¯ which is not normally known. This difficulty can be bypassed with the velocity form of the control algorithms, which do not need initialization.
It is protected against integral windup. The integral mode of a controller causes its output to continue changing as long as there is a nonzero error. Often the errors cannot be eliminated quickly enough and given enough time they produce larger and larger values for the integral term, which in turn keeps increasing the control action until it is “saturated” (e.g., the valve completely opens or closes). This condition is called integral windup. Then, even if the error returns to zero, the control action will remain saturated. The position form with its continuous summation of errors will produce integral windup and special attention will be required. The velocity form, on the other hand, is protected from integral windup for the following reason: The control action changes continuously until it becomes saturated. But then as soon as the error changes sign, the control action can return within the control range in one sampling period.
It protects the process against computer failure. With the velocity algorithm one can send out a signal which is used to drive an integrating amplifier or a stepper motor. These devices will retain the last calculated position of the control valve (or other final control element) in case the computer fails, thus avoiding total loss of control of the process.
2. PIC18F452 background
As mentioned earlier, the implementation is based on a Microchip PIC18F452 microcontroller, where the controller plays the role of the brain of the control system [5]. The right choice of the microcontroller is essential, as it will be the core of the final design. The PIC18F452 from Microchip has been chosen for the following advantages:
Speed: with its maximum internal clock rate of 20 MHz and its 16-bit-wide instruction bus, the CPU can execute most of its instructions at a single machine cycle of four clocks which is equivalent to a 0.2 μs.
Math support: unlike classical microprocessors, the controller in hand has got a hardware multiplier and divider for multiple-bytes, fixed-point numbers and for floating-point numbers so multiplication is carried out in a single instruction.
Flexible timer resources: four independent timers modules support timing measurements and output interval control with a timing resolution as fine as 0.1 μs. Those timers could be used to produce up to three pulse width modulations which could be used for electrical motor control.
Free software tools: Microchip’s Development Package MPLAB® (consisting of assembler, simulator, and user interface) as well as all manuals and application notes are available at no cost from their Web site (www.microchip.com).
Development tool versatility: it supports in-circuit debugger which permits the loading and execution of a user program as well as the use of breakpoints, memory/ register modification, and single stepping.
Build-in ADCs: it has analogue-to-digital converters with 10 bits resolution.
Built-in serial peripheral interface: it has a variety of serial bus interfaces like USART, I2C & SPI.
C programmable: it could be programmed using C language with the use of a variety of built in C libraries developed by microchip.
The PIC18F452 microcontroller is a 40 or 44-pin depending on the package, where in the 40 pins configuration, a dual inline package is used; whereas in the 44 pins configuration, either thin quad flat package or dual flat no leads package is used. Its design is based on Harvard technology where the program and data have different buses. This type of microcontrollers is very cheap, small in size, and could be customized. It could be easily programmed on-line using either assembly language, BASIC or C language. In fact, it is ideal for small application such as the one in hand. The controller has a 24 kbytes of flash memory and 2048 bytes of SDRAM. It also has a 8 × 10 bits analog to digital channels. It also has 5 bidirectional digital ports with 33 inputs/outputs, configured as follows: 3 × 8 digital I/O ports (PORTB, PORTC and PORTD), one six digital I/O port (PORTA) and one three digital I/O port (PORTE). Unfortunately, one of the drawbacks of microcontrollers, it is very seldom to find one with a digital to analog converter. Luckily, they are few manufacturers around including microchip, which make serial DACs which could be programmed through Serial Port Interface (SPI) using only three wires. The PIC18F452 has four timer/counters which could be programmed either as 8 or 16 bit timers/counters. It also has two ports which could be configured either as capture, compare or pulse width modulation (PWM). It has two serial peripheral interfaces: (SPI) and an inter-integrated circuit (I2C). An asynchronous port (USART) is also provided. For the microcontroller to output analogue data, an MCP4921 device is used. The device is a 12-bit buffered single voltage output Digital-to-Analog Converter (DAC). The device operates from a single 2.7 V to 5.5 V supply with an SPI compatible Serial Peripheral Interface. The user can configure the full-scale range of the device to be VREF or 2*VREF by setting the gain selection option bit (gain of 1 of 2). The user can shut down the device by setting the Configuration Register bit. In Shutdown mode, most of the internal circuits are turned off for power savings, and the output amplifier is configured to present a known high resistance output load (500 kΩ, typical). The device includes double-buffered registers, allowing synchronous updates of the DAC output using the LDAC pin. The device also incorporates a Power-on Reset (POR) circuit to ensure reliable powerup. The device utilizes a resistive string architecture, with its inherent advantages of low Differential Non-Linearity (DNL) error and fast settling time. The device is specified over the extended temperature range (+125°C). It provides high accuracy and low noise performance for consumer and industrial applications where calibration or compensation of signals (such as temperature, pressure and humidity) is required. The MCP4921 device is available in the PDIP, SOIC, MSOP and DFN packages. Figure 4 shows the chip pin configuration. The MCP4921 device is designed to interface directly with the Serial Peripheral Interface (SPI) port, which is available on the PIC18F452 microcontroller and supports Mode 0,0 and Mode 1,1. Commands and data are sent to the device via the SDI pin, with data being clocked-in on the rising edge of SCK. The communication is unidirectional; this means the data cannot be read out of the MCP4921. The CS (chip select active low) pin must be held low for the duration of a write command. The write command consists of 16 bits and is used to configure the DAC’s control and data latches. Register shown in Figure 5, details the write command which is loaded into the input register that is used to configure and load the DAC register [6].
Figure 4.
MCP4921 pin configuration.
Figure 5.
Write command register for MCP4921 (12-bit DAC).
The write command is initiated by driving the CS pin low, followed by clocking the four Configuration bits and the 12 data bits into the SDI pin on the rising edge of SCK. The CS pin is then raised, causing the data to be latched into the DAC’s input register. The MCP4921 utilizes a double-buffered latch structure to allow the analog output to be synchronized with the LDAC pin, if desired. By bringing the LDAC pin down to a low state, the content stored in the DAC’s input register is transferred into the DAC’s output register (VOUT), and VOUT is updated. The write to the MCP4921 device is 16-bit words. Any clocks past the 16th clock will be ignored. The Most Significant 4 bits are Configuration bits. The remaining 12 bits are data bits. No data can be transferred into the device with CS high. This transfer will only occur if 16 clocks have been transferred into the device. If the rising edge of CS occurs prior to that, shifting of data into the input register will be aborted. The most four significant bits are defined as follows:
bit 15 0 = Write to DAC register
1 = Ignore this command
bit 14 BUF: VREF Input Buffer Control bit
1 = Buffered
0 = Unbuffered
bit 13 : Output Gain Selection bit
1 = 1x (VOUT = VREF * D/4096)
0 = 2x (VOUT = 2 * VREF * D/4096)
bit 12 : Output Shutdown Control bit
1= Active mode operation. VOUT is available.
0 = Shutdown the device. Analog output is not available.
VOUT pin is connected to 500 kΩ (typical).
3. Liquid crystal display (LCD)
This module is designed to display the value of the temperature detected by the temperature sensor and to guide the user in changing the parameters of the controller. The LCD is a 16 × 2 alphanumeric display with the built-in Hitachi 44780 controller and LED backlighting. It works with an 8-bit data bus, which means it will require a total of 11 data lines. Three control lines (connected to port E) plus the 8 lines for the data bus (connected to port D) [7].
4. System design
The system is design around a stand-alone PIC18F452 controller, where the measured variable (MV) is read through channel0 (pin 2). The MV is subtracted from the set point automatically by the controller. The error is treated by the PIC PID and produces a digital control variable. This control variable is outputted through PIC serial data output pin (SDO pin 24) together with serial clock pin (SCK pin 18) to synchronize the conversion process. For the conversion to take place, the serial DAC chip select (CS) has to be pulled low. The CS is connected to pin RC0. The positive reference voltage is connected to +5 V (pin 6) and the negative reference voltage (pin 7) is tied to zero volt. The analog output is read through pin8 (Vout). This voltage is small to drive an electric motor. This voltage is pulled up to +12 V through the non-inverting operational amplifier (LM358). The Darlington transistor 2SD1409 is used to bust the current. The motor is connected to the emitter follower so that the driving current is sufficient enough to drive the motor. Needless to say that the diode 1N4148 is used to protect the Darlington transistor against any spike due to the change of current. Figure 6 shows the schematic of the system. The LCD is used to display the measured temperature. To manipulate the setting of different parameters, six push buttons are used as follows:
Figure 6.
System schematic circuit showing all the connection to the microcontroller, as well as the liquid crystal display and the final control element.
Six push buttons were used in the project to allow the user to change the setting and the controller parameters. Their functions are as follows:
Reset: To reset the microcontroller.
Stop: interrupt the program to allow the user to change the controller settings
Run: To run the program
Mode: To allow the user to change between setting modes.
Increment: To increment the controller variables by 1 or 0.1.
Decrement: To decrement the controller variables by 1 or 0.1.
These switches are connected to PIC PORTB to allow the user to use the internal build-in pull up resistors to prevent floating instead of using external pull-down resistors. The reset has got a separate button connected to MCLR pin. A buzzer is used as an alarm to indicate that the temperature is more than what the user specifies. Three LEDs were used to show the user the status of the microcontroller program. The three colors green, yellow, and orange were used as follows:
Green: means that the PID controller is working properly.
Yellow: means that the program is interrupted by (STOP) push button.
Red: means that the alarm is triggered.
5. Software design
To implement the control program, three major routines are used; the main routine along with the timer and external interrupts. The program starts with the main routine which contains all the configurations of the external pins whether outputs or inputs. It also contains the configurations of timer and external interrupts, so when one of these interrupts is triggered, the microcontroller will stop its current execution and perform another action. The trigger will be caused by either an overflow in timer register or a change on an external pin (RB0/INT0).
Because the time is a crucial element in digital control, the PID algorithm is controlled through a timer interrupt. This choice allows the user the ability to calculate the sampling time accurately. On the other hand, an external interrupt (INT0) is used to interrupt the program in order to allow the user a chance to modify the controller parameters. In the following we discuss in some details about the functions of each routine.
5.1 Main routine
5.1.1 Routine function
This routine, as mentioned earlier, is dedicated to configure the direction of external pins as well as interrupt sources. It also allows the user to choose the measured variable (temperature, flow, level or others). The flow chart of this routine is shown in Figure 7.
Figure 7.
Main routine.
5.1.2 External ports configuration
First PORTA (pin RA0) is configured as an analog input channel0 and PORTB as input digital port which is connected to the push button switches; while all other pins are configured as outputs.
5.1.3 LCD configuration
The configuration of the LCD was performed by separate software from Microchip called Application Maestro [8]. With the aid of this software, a configuration code was produced after modifying the module parameters. It was then incorporated into the project. Once incorporated, the LCD is configured and ready to work. One feature of using Application Maestro is its ability to use the prewritten code that this software provides to initialize or to write to the LCD.
5.1.4 Timer0 configuration
Timer0 can operate as a timer or as a counter. In Timer mode, the Timer0 module will increment with every instruction cycle (without prescaler). It is configured by setting a special function register called T0CON (timer0 control byte). This register is a readable and writable register that controls all the aspects of Timer0, including the prescale selection. In the design in hand, T0CON register is set to 0x85 (0b10000101) as shown below [9, 10].
This value will configure the timer0 as follows:
●Bit7
TMR0ON
= 1
: Timer0 is enabled
●Bit6
T08BIT
= 0
: Timer0 is configured as a 16-bit timer
●Bit5
T0CS
= 0
: Internal instruction cycle clock
●Bit4
T0SE
= 0
: This bit is used only with external clock
●Bit3
PSA
= 0
: Timer0 prescaler is assigned
●Bit2
T0PS2
= 1
: Bit2: T0PS2 =1:
}
1:64 prescaler value
●Bit1
T0PS1
= 0
: Bit1: T0PS1 =0:
●Bit0
T0PS0
= 1
: Bit0: T0PS0 =1:
5.1.5 Interrupt configuration
There are ten registers which are used to control internal and external interrupt operations to accommodate a variety of interrupts [11]. In the project in hand, only two interrupts are required INT0 and timer0 interrupt. To do so, only three control registers are required. These registers are INTCON, INTCON2, and RCON. INTCON register contains various enable bits as well as several interrupt flags. RCON is the Reset Control register which contains flag bits that allow differentiation between the sources RESET. Timer0 interrupt is enabled by setting TMR0IE bit (<5>) while external interrupt is enabled by setting INT0IE (INTCON<4>). Note that the interrupt flags are reset before enabling the interrupt in order to avoid unwanted interruptions.
To start the interrupt, the global interrupt bit GIE/GIEH (INTCON<7>) must be set. If set, it enables all unmasked interrupts, so if more than one interrupt source is used (as in our case) the Interrupt Priority Enable bit IPEN (RCON<7>) must be set and the interrupt sources should be specified either as high or low priority interrupt. The interrupt priority bit TMR0IP (INTCON2<2>) is used to specify the interrupt priority for Timer0. This bit is reset so timer0 interrupt is set to low priority. On the other hand, no need to specify the priority of the external interrupt (INT0), because it is already set to high priority by default.
After configuring the interrupts, the program will enter an infinite loop until one of the interrupt sources is triggered.
5.2 Timer interrupt routine
5.2.1 Routine function
The main purpose of this routine is to calculate the controller output and send it to the DAC serially through the synchronous SPI module [12]. Figure 8 shows the routine function.
Figure 8.
Timer interrupt routine.
5.2.2 Timer reloading
Because of the importance of time in calculating the timed controller output, timer0 is used as an accurate hardware timer. The source clock of the timer is the crystal oscillator which is fed to the clock pin of Timer0 internally. The clock used is a 20 MHz derived from a stable crystal oscillator. This frequency is automatically divided by 4 because the controller machine cycle is 4 clocks to give a 5 MHz which is fed to the timer. The timer is exactly clocked every 0.2 μs and takes 13107.2 μs (16-bit mode) to count from zero to zero again. However, by loading the timer with a suitable value, a smaller time interval could be obtained. For example, by loading the timer with the value 4095 (0xFFF), the overflow would occur after 12288.2 μs. Alternatively, the time period can be extended by using a prescaler as was done in the main routine. If a divide by 64 prescaler is selected, timer0 only overflows after 838.848 ms. This is obtained as follows:
5MHz64=78,125Hz
78125Hz−1=12.8μs
12.8μs×65,535=838.848ms
This time period is less than one second, while a one second sampling time is required for the design in hand. To obtain a one second sampling time, the timer should count 78,125 pulses.
Because timer0 register is only 16 bit wide, it is only limited to count up to 65,535 pulses. The interruption is trigged several times to obtain one second timing, after which the controller computes the control action and sends it to the DAC. By using MPLAP simulator, it was found that 5362 cycles are required to calculate the controller output and send it to the DAC besides 51 extra cycles needed to reload the timer with time constant. If the interruption is required to repeat itself five times before calculating the controller output, one needs 5362 + 51 × 5 = 5617 cycles (1.1234 ms). Thus, in order to get exactly one second sampling time, the timer register (TMR0) has to be reloaded with a value that interrupts the program every 998.8766 ms (1 s–1.1234 ms). The following shows how this value is obtained:
No.of cycles for1s=5×106cycles
Thereforeno.of cycles between interrupts=5×106−5617=4,997,383cycles
By using a timer with 64 prescaler:
No.of counts=4,997,38364=78,084.10938
When we repeat the interrupt for 5 times:
No.of counts=78,084.109385=15,616.82188
But because the timer counts in ascending order (from 0x0000 to 0xFFFF):
Reload value=65,535−15616.82188=49918.17812
repeat=5*sampling_time;
term_1=((repeat)*51.0+5362.0);
term_2=(5000000.0*sampling_time-term_1)/64.0;
term_3=term_2/repeat;
cycle=65535-term_3;
However, the timer register accepts only integer numbers, thus the final value that should be added to the timer register is 49918. Because we omitted the numbers after the decimal point, our error will be ± 1 count which is equal to 64 cycles. Therefore, our error in calculating the sampling time will be:
Timer error=64×0.2μs=12.8μs
This calculation is for getting 1 s sampling time. To expand the calculation in order to enable the user to change the sampling time, one defines two integer variables (repeat and cycle). The first variable repeat is to determine how many times we need to repeat the interrupt, while the second one cycle is the final value that should be added to the timer register. The following pseudo code shows the general formula used to reload the timer register.
5.2.3 Analog to digital converter module
The ADC module normally operates at 10-bits resolution, giving output digital values 0–1024 [13]. It needs a reference voltage to set the maximum and minimum values for the input conversion. This reference can be provided internally as Vdd and Vss (supply values) or externally through Vref+ and Vref− pins. To configure this module, OpenADC function from Microchip C library is used. This function performs a bitwise AND operation (“&”) between its arguments which are defined in the file adc.h. The parameters of this function along with their meaning of each argument are discussed below [1]
A clock divider to allow the minimum specified conversion time (about 20 μs). A 32 prescaler was chosen because the clock source is 20 MHz
ADC_RIGHT_JUST: Right justified.
Because the ADRES register pair (where the converted values are loaded) is 16-bit wide. But the ADC is only 10bit wide. The ADC module could either be configured as right or left justified. In this project, right justified is chosen as shown in Figure 9. This sets the 6 most significant bits of register ADRES to zeros.
ADC_8ANA_0REF: VREF+ = VDD, VREF− = VSS
The supply values are chosen as the voltage references to the ADC.
ADC_CH0: Channel0 (AN0) is selected
ADC_INT_OFF: Interrupts of ADC interrupts are disabled.
Once the A/D conversion is completed, the result is stored in an integer variable called result. After reading the analog value by the ADC module, the result will be compared with the variable alarm-trigger which was previously specified. If the result is greater than this value, the microcontroller triggers the buzzer and lights the red LED.
Figure 9.
Choosing right justified for data input.
5.2.4 Controller calculation
Due to the limitation in the microcontroller’s memory, the PID equation is divided into three terms (term_1, term_2, and term_3) and after calculating each term separately, they are added together along with the previous output to give the controller output which will be sent to the DAC. The following code shows how to calculate the controller output
To send the control variable to the final control element, the serial DAC, which is interfaced to the Serial Peripheral Interface (SPI) port, is used. The SPI is initiated using Microchip C library called OpenSPI. This function also performs a bitwise AND operation between its arguments which are defined in the file SPI.h according to the following formula.
SPI_FOSC_16: Master mode and the clock = FOSC/16
MODE_00: Mode 0,0 (change takes place on the rising edge)
SMPEND: Input data sample at end of data out
OpenSPI(SPI_FOSC_16, MODE_00, SMPEND);
After configuring the module, it is time to write a command to the DAC in order to convert it into analog signal. The write command is initiated by driving the CS pin low, followed by clocking the four configuration bits and the 12 data bits into the SDI pin on the rising edge of SCK. The CS pin is then raised, causing the data to be latched into the DAC’s input registers and when the LDAC pin is pulled down through RC1, the values held in the DAC’s input registers are transferred into the DAC’s output registers to provide the analog signal. It is important to mention here that we wrote the write command in two steps (as shown in the following code) because the SPI module send only 8 bit at a time.
LATCbits.LATC0 = 0; // Chip Select is set
WriteSPI(pid_output_16_high); //4MSB as command + 4LSB as data
WriteSPI(pid_output_16_low); // 8 bit data
LATCbits.LATC0 = 1; // Chip Select is reset
LATCbits.LATC1=0; // enable LDAC for data output to DAC
LATCbits.LATC1=1; // disable LDAC
CloseSPI();
5.2.6 Writing on the LCD
To write characters to the LCD, required prewritten functions are provided by Application Maestro. Some of These functions are listed in following table:
XLCDInit()
It is used to initialize the LCD module according to the Application Maestro options
XLCDPut(data)
It sends the clocking signal and data to be displayed to the LCD
XLCDL1home()
Points to the first address location of line one of the LCD
XLCDL2home()
Points to the first address location of line two of the LCD
XLCDClear()
Clears the DDRAM content of the LCD and points to the 00 address location
XLCDPutRomString(addr)
Displays String in Program memory
XLCDPutRamString(addr)
Displays String in Data memory
XLCDCommand(Command)
It sends clocking signal and instructions to the LCD
For numbers to be displayed, they are first converted into strings (characters) before being sent to the LCD, since the latter only accepts strings. To do so a C function called sprintf is called upon. This function saves the number in an array after converting it into string. The subroutine to do so is shown below [4]:
sprintf (buf,"%d",temp_set);
XLCDPutRamString(buf);
5.3 External interrupt routine
5.3.1 Routine function
The main function of this routine is to allow the user to change the controller parameters. The routine is initiated by pressing the push button (STOP) which is connected to the external interrupt pin (RB0/INT0). Once initiated, the user is able to change all the parameters of the controller (KC, τI, τD, sampling time, alarm trigger and sensitivity) by using three push buttons (MODE, INCREMENT and DECREMENT [11].
To determine which action the microcontroller should take if any push button is pressed, we defined two integer variables (present_button and present_mode) to be used as statuses. That is, each bit of them has specific meaning as described below:
present_button
Np
____
____
____
____
____
____
____
____
____
____
____
____
dec
inc
Mod
bit15
bit0
● bit 15
Np: set if there are no push buttons pressed
● bit 14-3
Unimplemented
● bit 2
dec: set if the DECREMENT push button pressed
● bit 1
inc: set if the INCREMENT push button pressed
● bit 0
mod: set if the MODE push button pressed
present_mode
____
Srt
Spt
KC
TI
TD
Stm
Sen
Alm
Tun
Dp
Tp
Kp
____
____
____
bit15
bit0
● bit 15
Unimplemented
● bit 14
Srt: set in the starting mode
● bit 13
Spt: set in the set point mode
● bit 12
KC: set in the controller gain mode
● bit 11
TI: set in the integral time mode
● bit 10
TD: set in the derivative time mode
● bit 9
Stm: set in the sampling time mode
● bit 8
Sen: set in the sensitivity mode
● bit 7
Alm: set in the alarm mode
● bit 6
Tun: set in the tuning mode
● bit 5
Dp: set in the process delay mode
● bit 4
Tp: set in the process time constant mode
● bit 3
Kp: set in the process gain mode
● bit 2-0
Unimplemented
Initially, before pressing any push button, present_button variable is loaded with 0x8000 (no push button pressed), and present_mode with 0x2000 (starting mode).Then if any push button is pressed, the corresponding bit of that push button will be set, giving a specific value of present_button which indicates the push button that was pressed by the user. So by performing a bitwise OR operation between the two variables (present_button and present_mode) we will come up with a number indicates the push button pressed and the present mode and based on that number we can decide the proper action to be taken by the microcontroller. The following code shows how to perform the OR operation after checking which of the push buttons was pressed. Beside changing the controller variables, this routine has another feature, it gives the user preliminary values of the controller parameters after entering the process variables. The result is derived based on Cohen-Koon tuning method. However, this feature is impractical if the sampling time is big [14].
if (mode_pin==0){
Delay10KTCYx(70);
present_button=mode_pushed;
}
else if (inc_pin==0){
Delay10KTCYx(70);
present_button=inc_pushed;
}
else if (dec_pin==0){
Delay10KTCYx(70);
present_button=dec_pushed;
}
else if(end_pin==0)
return;
action= present_mode | present_button;
6. Testing and verification
To test the system, a first order system given by the equation below was used. To run the control action, the system was converted into a difference equation given by Eq. (10).
Gp=105S+1E9
The process transfer function is first order, thus the discrete transfer function obtained using Zero-Order Hold will be:
HGz=az−11−bz−1E10
where:
a=kp1−exp−Tsτp
b=exp−Tsτp
If kp = 10, τp = 5, and Ts = 1, The discrete transfer function will be:
HGz=1.813z−11−0.8187z−1E11
⇒1−0.8187z−1Yz=1.813z−1Cz
Yz−0.8187z−1Yz=1.813z−1Cz
yn−0.8187yn−1=1.813cn−1
Therefore the difference equation of the output is:
yn=0.8187yn−1+1.813cn−1E12
After getting the difference equation, the control scheme was tested and the output of Figure 10 was obtained with the parameters set to: Kc = 0.2, Ti = 4.0 and Td = 0.0. The parameters were then changed to: Kc = 0.1, Ti = 3.0 and Td = 0.2. The response is shown in Figure 11.
Figure 10.
Controller response with Kc = 0.2, Ti = 4.0 and Td = 0.
Figure 11.
Controller response with Kc = 0.1, Ti = 3.0 and Td = 0.2.
7. Conclusion
By referring to the previous graphs, it could be concluded that the response tracks the set point as expected. In addition, the increase in controller gain (Kc) does speed up the response but at the expense of the overshoot. Based on these results, it could be concluded that the three-term controller is working according to plan. Because of the flexibility of the microcontroller and its programming, any control scheme could be developed and implemented in the manner as described in this chapter. Not only that, the scheme could be transferred to several high range microcontrollers from the same company such as 16 or 32 bits with the use of the benefits those types of controllers offer.
\n',keywords:"PIC microcontroller, PID, timer, Digital to analog converter, serial digital to converter, liquid crystal display",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/70956.pdf",chapterXML:"https://mts.intechopen.com/source/xml/70956.xml",downloadPdfUrl:"/chapter/pdf-download/70956",previewPdfUrl:"/chapter/pdf-preview/70956",totalDownloads:179,totalViews:0,totalCrossrefCites:0,dateSubmitted:"April 19th 2019",dateReviewed:"September 19th 2019",datePrePublished:"January 31st 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Microcontrollers are used in almost any applications that come across one’s mind, from small control applications such as home appliances to aerospace. Microcontroller-based controllers are cost-effective and flexible to modify the design to meet the requirement for any control of any industrial plant. Microcontrollers do not require external hardware interface, memories, counter/timers, and ADCs, because they are all integrated inside the chip. Those controllers could be programmed online and do not require any backup memories except for big applications. This chapter presents the implementation of the three-term PID controller using a Microchip PIC18F452 microcontroller. To read data into the controller, a 10-bit integrated ADC is used; and to read data out of the machine, an external 12-bit serial DAC is used. Before programming the PIC microcontroller, the task to be tested could be off-line using a software simulator to make sure that it is working according. When that is the case, it could be then fired into the controller on-line in a mater seconds. Not only that, if the user decides to use different algorithm, he only programs the controller again online.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/70956",risUrl:"/chapter/ris/70956",signatures:"Mostefa Ghassoul",book:{id:"9385",title:"Renewable Energy",subtitle:null,fullTitle:"Renewable Energy",slug:null,publishedDate:null,bookSignature:"Associate Prof. Tolga Taner",coverURL:"https://cdn.intechopen.com/books/images_new/9385.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"197240",title:"Associate Prof.",name:"Tolga",middleName:null,surname:"Taner",slug:"tolga-taner",fullName:"Tolga Taner"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"146702",title:"Dr.",name:"Mostefa",middleName:null,surname:"Ghassoul",fullName:"Mostefa Ghassoul",slug:"mostefa-ghassoul",email:"mghassoul@uob.edu.bh",position:null,institution:{name:"University of Bahrain",institutionURL:null,country:{name:"Bahrain"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. PIC18F452 background",level:"1"},{id:"sec_3",title:"3. Liquid crystal display (LCD)",level:"1"},{id:"sec_4",title:"4. System design",level:"1"},{id:"sec_5",title:"5. Software design",level:"1"},{id:"sec_5_2",title:"5.1 Main routine",level:"2"},{id:"sec_5_3",title:"5.1.1 Routine function",level:"3"},{id:"sec_6_3",title:"5.1.2 External ports configuration",level:"3"},{id:"sec_7_3",title:"5.1.3 LCD configuration",level:"3"},{id:"sec_8_3",title:"5.1.4 Timer0 configuration",level:"3"},{id:"sec_9_3",title:"5.1.5 Interrupt configuration",level:"3"},{id:"sec_11_2",title:"5.2 Timer interrupt routine",level:"2"},{id:"sec_11_3",title:"5.2.1 Routine function",level:"3"},{id:"sec_12_3",title:"5.2.2 Timer reloading",level:"3"},{id:"sec_13_3",title:"5.2.3 Analog to digital converter module",level:"3"},{id:"sec_14_3",title:"5.2.4 Controller calculation",level:"3"},{id:"sec_15_3",title:"5.2.5 SPI module",level:"3"},{id:"sec_16_3",title:"5.2.6 Writing on the LCD",level:"3"},{id:"sec_18_2",title:"5.3 External interrupt routine",level:"2"},{id:"sec_18_3",title:"5.3.1 Routine function",level:"3"},{id:"sec_21",title:"6. Testing and verification",level:"1"},{id:"sec_22",title:"7. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Ogunnaike BA, Ray WH. Process Dynamics, Modeling, and Control. 1st ed. USA: Oxford University Press; 1994'},{id:"B2",body:'Stephanopoulos G. Chemical Process Control: An Introduction to Theory and Practice. USA: Prentice Hall; 1983'},{id:"B3",body:'Seborg DE, Edgar TF, Mellichamp DA, Doyle FJ. Process Dynamics and Control. 4th ed. New York, USA: John Wiley and Son; 2017. pp. 115-117'},{id:"B4",body:'Phillips CL, Nagle HT, Chakrabortty A. Digital Control System Analysis and Design. UK: Pearson; 2015. pp. 279-335'},{id:"B5",body:'Microchip PIC18FXX2 Data Sheet “High-Performance, Enhanced Flash Microcontrollers” Microchip 2006 (DS39564C)'},{id:"B6",body:'Microchip MCP4921/4922 datasheet “12-Bit DAC with SPI™ Interface” Microchip 2007'},{id:"B7",body:'HD44780 LCD starter guide. 2001'},{id:"B8",body:'Microchip application maestro software user’s guide. 2003'},{id:"B9",body:'Microchip PIC18FXX2 Data Sheet “High-Performance, Enhanced Flash Microcontrollers: Timers” Microchip 2006 (DS39564C). pp. 103-115'},{id:"B10",body:'Bates M. PIC Microcontrollers: An Introduction to Microelectronics. Newnes; 2011'},{id:"B11",body:'Microchip PIC18FXX2 Data Sheet “High-Performance, Enhanced Flash Microcontrollers: External interrupts” Microchip 2006 (DS39564C). pp. 73-85'},{id:"B12",body:'Microchip PIC18FXX2 Data Sheet “High-Performance, Enhanced Flash Microcontrollers: Timer0 interrupt” Microchip 2006 (DS39564C). p. 85'},{id:"B13",body:'Microchip PIC18FXX2 Data Sheet “High-Performance, Enhanced Flash Microcontrollers: 10 bit ADC” Microchip 2006 (DS39564C). Chandler, Arizona USA; pp. 181-188'},{id:"B14",body:'Deitel PJ, Deitel HM. C How to Program. 6th ed. New Jersey USA: Pearson Prentice Hall; 2010'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Mostefa Ghassoul",address:"mghassoul@uob.edu.bh",affiliation:'
Chemical Engineering, University of Bahrain, Bahrain
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\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
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\n\t
Does your institution already have a budget for covering Open Access publication costs?
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Does your grant list Open Access publication fees as legitimate direct/indirect costs?
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\n\n
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\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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