THE APPLICATION OF STATISTICAL QUALITY CONTROL TOOLS TO MONITORING ETHANOL PROCESS PRODUCTION.

Transcription

1 1 XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO THE APPLICATION OF STATISTICAL QUALITY CONTROL TOOLS TO MONITORING ETHANOL PROCESS PRODUCTION. Juliana Keiko Sagawa (UFSCAR ) juliana@dep.ufscar.br Ricardo Inoue Yamada (UFSCAR ) ricardo.yamada@hotmail.com O presente trabalho tem como objetivo apresentar a aplicação de ferramentas de Controle Estatístico da Qualidade ao processo de produção de etanol a partir da cana-de-açúcar. As ferramentas foram aplicadas às etapas de Fermentação e Tratameento do Fermento em uma usina localizada na região de Guariba, interior do estado de São Paulo. Tais etapas apresentam alto grau de complexidade, englobando tanto reações físicas como bioquímicas, e impactam diretamente na eficiência da produção de Etanol. As variáveis do processo de fermentação e de tratamento do fermento foram previamente relacionadas e a partir de uma análise crítica e estruturada, foi possível identificar quais etapas e variáveis necessariamente deveriam ser monitoradas. As análises dos dados amostrais permitiram a identificação dos índices de capabilidade do processo (Cpk). Como contribuição, o estudo permitiu a identificação das variáveis com maior instabilidade, o que, aliado às análises dos resultados (Produção Total de Etanol), foi determinante para estimar os impactos do controle para o processo, justificando assim sua aplicabilidade. Palavras-chaves: SPC - Statistical quality control, Control charts, Quality management, Sugar cane and Ethanol.

2 .1 1. Introduction Although in most countries the censuses have indicated a decrease in the birth rate, recent analyses published by the United Nations Population Fund (UNFPA), foresaw a projected population growth of more than 25% by Estimates indicate that the world population will exceed 8.9 billion by that year. According to the Intergovernmental Panel on Climate Change (IPCC), the equation used to measure the increasing trend in CO2 emissions and their impacts on climate changes, such as global warming, is under direct influence of the population growth and the increase in gross domestic product per capita worldwide. In February of 2010, the bioethanol produced in Brazil using sugarcane was recognized by the United States Environmental Protection Agency (EPA) as an advanced biofuel. Tests showed a reduction in the emission of greenhouse gases by 61% compared to the emissions produced by gas. This recognition is accorded to those initiatives that reduce the emissions of greenhouse gases in at least 50%. According to Costa et al. (2005), major changes in production management have been observed over the last 60 years, but two points are worth mentioning: the first is the advancement in technology and technological development applied to information management, which contributed to a more efficient control of operations; the second, but no less important, is related to the new concepts and methods of production management. These methods started to gain prominence in the 80s, more specifically with the spread of the concepts of quality management in the United States and Japan. Although its development has emerged in the 20 s, the Statistical Process Control (SPC) came to be applied effectively in the Western companies in the 80 s, when they were forced to improve their quality of its products to better serve the demands of their consumers. According to Martins (2010), many Brazilian companies have not yet identified the advantages in the use of SPC to control the variations in their processes and consequently ensure greater uniformity of their products and services. The following paper presents a case study of the application of Statistical Quality Control tools in the critical steps of the production process of ethanol from sugar cane. More specifically, the process of fermentation and treatment of yeast are approached. 2

3 2. Research methodology The present research was developed according to the following steps: Theoretical study and literature review on Statistical Quality Control; Characterization of the ethanol production process; Selection of relevant variables of the ethanol production process; Implementation of Control Charts and Process Capability Analysis. The following words were uses to search the literature: Statistical Process Control (SPC), Sugar Cane, Ethanol, Control Charts and Statistical Quality Control. As search web sites were used: Scielo, Virtual Libraries (USP, UNICAMP, UFSCar) and Google Scholar. In this study, the researcher was an observer and a participant. Thus, the data collection process was based on direct observation, meetings with the technical team responsible for the project, formal documents, charts and informal conversations. The analysis and selection of the critical variables of the production process were carried out by the technical team by means of brainstorming. 3. Literature review In the following subsections, a short literature review on statistical process control, control charts, capability analysis is presented, as well as a characterization of alcoholic fermentation processes Statistical process control According to Oliveira (2010), the permanent monitoring of processes is needed, especially for detecting the presence of special causes that generate disturbances in the process, also serving as base for making decisions. The disturbances that affect the processes may be classified into two types. Minor perturbations caused by natural variations in process, derived from an ordinary or random cause, represents small deviations that do not compromise or are negligible to the result. The special causes, on the other hand, are major perturbations that can shift the average of its target!], as well as increase its dispersion. The perturbances are usually derived from problems or abnormal operations, are mostly related to physical conditions and structural projects or deficiencies in standards work. Special causes of variation are caused by know factors that lead to an unexpected change in the process output. If the process is subjected to Special Causes of variation, the process output is not stable over time, it is not predictable. The special causes may lead to a process shift. 3

4 According to Montgomery (2004), the SPC has a powerful collection of tools for troubleshooting that can be applied to any process, its seven main tools are: Ishikawa (fishbone) Diagram, Check Sheet, Histogram, Pareto Chart, Scatter Diagram, Flowchart and Control Chart Control chart According Montgomery (2004), the control charts for variables are used when the monitored variable can assume numerical values on a continuous scale, and enable the identification of special causes in a process out of statistical control. However, an important issue that must be mentioned is that these graphs only indicate the presence or absence of these causes; they do not exclude the need for an analysis of which are these causes that are acting in a process and how to eliminate them. As is known, the chart has three horizontal lines that represent the limits previously measured or calculated by sampling of a random variable. The Central Limit or Target (T), represents the average value of the variable and which also corresponds to the control state. The two other lines, positioned at the ends of the Target (T), are: Upper Specification Limit (USL) and Lower Specification Limit (LSL), which represents the control limits that the sampling points should be between while the process is under control. According Montgomery (2004), in cases where it is possible to establish predefined values as references for average and standard deviation, these values can be used for Chart of the average X and Chart of amplitude R without the need to analyze historical database to establish the target and the upper and lower specification limits. Generally the values of the population mean (μ) and standard deviation (σ) must be estimated from samples taken from the controlled process in order to calculate the control limits. Also according to Montgomery (2004), caution is needed when the values of Mean (μ) and standard deviation (σ) are already known and referenced, it is possible that these standards are not really applicable to the process, so may produce many alerts out of control Process-capability analysis According Montgomery (2004), the magnitude of CpK index is a direct measure of how offcenter the process is operating, in other words, it considers not only the variability of the process, but is also sensitive to process shift. For analyzing and interpreting the CpK index results were used reference ranges listed in Table 1. Table 1 - Classification of processes from the CpK index. 4

5 CpK Index Classification Interpretation The capacity of the process is inappropriate CpK < 1,00 Unstable Process to the required specification. The capacity of the process is within the 1,00 < Cpk < 1,33 Partially able Process required specification. The capacity of the process is adequate to Cpk 1,33 Stable Process the required specification. Source: Montgomery (2004) 3.4. Characterization of the alcoholic fermentation process The ethanol extracted from sugarcane is obtained by alcoholic fermentation. It consists on a biological process in which sugars, present in the sugarcane juice are converted into cellular energy and thereby produce ethanol and carbon dioxide as metabolic waste products. According Basso et al. (2001), the yeast Saccharomyces cerevisiae, popularly known as baker's yeast, is the most common specie used for ethanol production. It is a facultative aerobic fungus and the products obtained from sugar metabolizing vary with the environmental conditions in which they are taken. In anaerobic reactions, the metabolized sugar is converted into ATP, i.e. the cellular energy necessary for survival and cellular growth of the yeast, producing ethanol and carbon dioxide. For the best performance in the conversion of sugar into ethanol, it is important to evaluate and control the changes in the conditions of fermentation, such as pressure, temperature, ph, oxygenation, substrate, species, Lineage, and other contaminations (BASSO et al., 2001) According to Lopes (2008), the fermentation process can be divided into five stages: Lag-phase: An adaptation stage where the enzyme reconstruction and the cellular multiplication occur; an increase in the amount of cells present in the mash is observed. Acceleration phase: in this stage the speed of cellular multiplication increases and the sugar in mash begins to be metabolized. Exponential phase: as the name says, in this stage there is an exponential increase in the number of cells, characterized by the large amount of metabolic waste products obtained, such as Ethanol. Stationary phase: This stage is marked by the exhaustion of nutrients and sugars present in mash, which ensures the required energy for the emergence of new cells. As consequence, the number of cells is kept constant. 5

6 Decline phase: in this stage is observed a drop in viability of the yeast. In other words, the number of cells that dies is bigger than the number of new cells. This happens due to a deficiency in maintaining the necessary conditions of temperature and ph of the fermented wine. In addition, the ethanol present in fermented wine destroys the cell membrane of yeast, favoring infections. According to Lopes (2008), the product obtained after fermentation goes through a centrifuge. The yeast cream that is separated from the wine is recovered and treated with water, lowering its concentration from 60% down to approximately 25%. Acid or an antibiotic is also added to the yeast in order to reduce bacterial contamination. Then, it is pumped back to the yeast treatment vat and re-added to the next fermentation. The resulting fermented wine is sent to the distillation process, where the hydrated Ethanol is separated from the other components with different boiling points. Chemical treatments of dehydration can be used to reach the specifications of 99.7 GL, resulting in the anhydrous ethanol used for blending with pure gasoline. 4. Case study applying statistical quality tools to the ethanol production The following items present the steps and results of the case study carried out in a chemical company, which illustrates the application of quality tools to monitor the ethanol production process Definition of the transformation steps Initially, the macro phases of the ethanol production were identified. After analyzing the nature of its operations, the production process was divided in three steps, as proposed in Figure 1. Figure 1 - Macro steps of Hydrated Ethanol production. Source: own author 6

7 First step: the raw material mix is treated to provide favorable conditions for the following biochemical reactions. Second step: the mix (called mash) will be metabolized by the reactor (yeast). Third step: the metabolized products will be taken to additional physical treatments until reaching the desired specifications. Although comprehensive, this representation of the production process does not present the required level of detail to allow the identification of the critical variables of the process. Thus, the technical group in charge of the project mapped the production process with more detail, as shown in Figure 2. Figure 2 - Flow of the stages of production of Ethanol. Source: own author As it can be seen in Figure 2, the clarified sugarcane juice, the molasses and the water are mixed together to form the mash, which is boiled and then cooled down to a specific temperature. After that, the yeast is added and the fermentation process occurs under controlled conditions. The resulting mix is filtered and centrifuged, allowing the separation of the wine from the yeast cream, finally, is distilled to yield ethanol, while the yeast is treated to be reused in the next fermentation process, as mentioned Identification of the critical steps After mapping the production process, the project group decided to specifically focus on the fermentation and the yeast treatment processes. These steps were considered critical since the 7

8 ethanol production efficiency directly depends on the success of the fermentation reactions, which, on their turn, require the correct control of several variables. These variables will be discussed as follows Identification of critical variables By means of a brainstorming, the technical group responsible for the project identified X variables of influence on the fermentation process. Given the large number of variables identified, a criterion was established to prioritize them according to their degree of relevance to the process performance indicators. The following performance indicators were considered: milling capacity, loss in distillation, loss in final effluent, undetermined loss, accident risks, and costs of non-quality. First of all, the group of experts in the area assigned a score to each performance indicator to reflect its impact on the process results according to the following scale: 1 - Low impact / 2 - Medium impact / 3 - High impact. Each score was given after the group reached a consensus. Figure 3 - Calculation Methodology Source: own author Similarly, the experts assessed the correlation of the process variables to the performance indicators, that is, they evaluated the extent with which a given process variable would affect a given performance indicator. Grades were assigned according to the following scale: 0 - Nonexistent Correlation / 1 Weak Correlation / 3 Median Correlation / 9 Strong Correlation. In order to obtain a prioritization index, a weighted sum of grades was calculated for each variable, as shown in Equation 1 below: Zi = (Y1. Xi1) + (Y2. Xi2) + (Y3. Xi3) + (Y4. Xi4) + (Y5. Xi5) + (Y6. Xi6) + (Y7. Xi7) As a result of the analysis, 7 variables were classified as critical: Temperature in the fermentation vats, Alcoholic concentration in the fermentation vats, Brix of the mash, Temperature of the mash, Alcoholic concentration in the vats of yeast treatment, Viability in the fermentation vats and Infection in the fermentation vats. For monitoring the critical 8

9 variables using control charts, the group previously identified the impacts of each critical variable on the fermentation process and defined the sampling frequencies of the variables. In addition, a bank of actions to correct deviations was created for each variable, enabling to solve problems as quickly as possible and decentralizing the power of decision making. 5. Capability of critical variables For the ethanol production process, previous research has shown that optimum results are obtained if some critical variables remain between specified intervals. Thus, the specification limits of variation for these variables were already established from empirical studies. In case study, rather than conduct a sample analysis of historical data to establish values of Target, Upper and Lower Specification Limits of the process were used these pre-specified values referenced in Table 1. Table 2 - Analysis of the index Cpk for different periods. Source: own author Two hundred daily samples of each variable were provided by the industrial laboratory in order to plot the control charts. The data was divided into 4 periods, each of them with 50 daily samples on chronological order. The variables analysis was carried out only for two periods, the worse and the better performance in the Ethanol production, respectively represented by Period 1 and Period 2 in Table 1. Samples suffered some interruptions due to equipments stoppage during rain periods. This is a particularity of the ethanol production process. An important evaluation about the monitoring importance and their effects in results of total ethanol production is that in Period 2 was observed an increase in total production of ethanol over 26% compared to Period 1. The verification of special causes acting in the process can be done using Control Charts and capability analysis. The impacts of each critical variable in the Ethanol production process as well as the results of capability analysis of these variables are provided bellow: 9

10 Temperature in the fermentation vat (ºC) Analysis of Cpk Period 1:Unstable process (0.77) Period 2: Stable process (1.59) When below the LSL: Influences the speed and productivity of fermentation processes. When above the USL: Possibly reduces cellular viability of the yeast, due to an increased probability of infection and flocculation in mash. Histogram observations Distribution with low variability, however, the variable is off-center. Alcoholic concentration in the fermentation vats (ºGl) When below the LSL: Causes residual losses in fermentation processes. When above the USL: Possibly reduces the cellular viability of yeast, due to excessive exposure to high alcoholic level. Analysis of Cpk Period 1: Unstable process (0.11) Period 2: Unstable process (0.39) Histogram observations Distribution with moderate variability and off-center. Brix of the mash (ºBrix) Analysis of Cpk Period 1: Unstable process (0.57) Period 2: Partially able process (1.11) When below the LSL: Reduces the fermentation time and also the process efficiency. When the above USL: Increases the concentration of alcohol on mash, influencing cellular viability losses. Histogram observations Distribution with low variability and somewhat off-center. Temperature of the mash (ºC) 10

11 Analysis of Cpk Period 1: Unstable process (0.40) Period 2: Unstable process (0.14) When below the LSL: Reduces the speed of fermentation process and consequently decreases productivity. When above the USL: Favors bacterial contamination by proliferation, reducing the cellular viability. Histogram observations Distribution with moderate variability and off-center. Alcoholic concentration in the vats of yeast treatment (ºGl) When below the LSL: Favors yeast growing, depleting nutrients dosed for their treatment. When above the USL: Inhibits cellular growth by inhibiting yeast recovery. Analysis of Cpk Period 1: Unstable process (0.95) Period 2: Unstable process (0.43) Histogram observations Distribution with moderate variability and significantly off-center. Viability in the fermentation vats (%) When below the LSL: Causes reduction of metabolic reactions efficiency, resulting in a worse performance of fermentation processes. When above the USL: Unilateral variation, USL is the best reachable result. Analysis of Cpk Period 1: Unstable process (-0.87) Period 2: Unstable process (-0.52) Histogram observations Distribution with high variability and significantly off-center. Infection in the fermentation vats (x10 7 ) 11

12 When below the LSL: Unilateral variation, LSL is the best reachable result. When the above USL: Reduces the cellular viability as consequence of increased bacterial infections. Analysis of Cpk Period 1: Unstable process (-0.15) Period 2: Unstable process (-0.10) Histogram observations Distribution with high variability and significantly offcenter. As it could be observed from the analysis of the histograms, the variables were not centered at the midpoint of the specifications. 6. Concluding remarks The objective of the presented study was to illustrate the application of Statistical Process Control tools in Ethanol production processes. For this purpose, data was collected by means of direct observation, interviews and document analysis. During the study execution, control charts were plotted and capability analysis was carried out. Observed deviations were investigated and some causes of the problem were identified using methodologies for analysis and troubleshooting, such as statistical and quality tools. Other actions included training the employees involved in Fermentation and Yeast Treatment processes. In general, the study demonstrated that principles of Statistical Quality Control could be widely applied in Ethanol production processes. The quality tools allowed the diagnosis of which variables should be controlled to improve the alcoholic fermentation efficiency. Moreover, this diagnosis indicated which processes need improvement actions in terms of variability reduction and in terms of systematic error corrections. Based on presented results, some variables presented low or moderate variability, as: Temperature on the fermentation vat, Brix of the mash, Alcoholic concentration in the fermentation vats, Temperature of the mash and Alcoholic concentration in the vats of yeast treatment, although, the average values are significantly off-center in relation to their specifications. Such variables require the adoption of corrective measures to reverse the systematic average deviations. Other variables, besides being off-center, also presented high variability, as: Viability in the fermentation vats and Infection in the fermentation vats. For such variables, it is necessary not 12

13 only to adopt measures to eliminate systematic average deviations, but also actions to reduce variability. For processes improvement, suggests an permanent and constant variables analysis, sharing and providing the analysis in a structured and organized form. Measure and compare progress towards the target, turning them into actions to correct problem causes and suiting the processes to achieve better results. Using tools for analyzing and troubleshooting, attacking the problems at their root causes, improving work standards, as well as equipment and installations. REFERENCES COSTA, A. F. B; EPPRECHT, E. K. & CARPINETTI, L.C.R. Controle Estatístico de Qualidade. 2º Ed. São Paulo: Atlas, IPCC. Renewable Energy Sources and Climate Change Mitigation Cambridge University Press, Cambridge, United Kingdom, p. Disponível em: < publications_and_data_reports.shtml#srren > Acesso em: 11 jan LIMA, U. A.; BASSO, L. C.; AMORIM, H. V. Produção de Etanol. In: LIMA, U. A. et al. (Coord.). Biotecnologia Industrial: Processos Fermentativos e Enzimáticos. São Paulo, Edgard Blücher, v. 3, LOPES, M. M. Estudo comparativo da destilação em batelada operando com refluxo constante e com composição do destilado constante. Dissertação (Mestrado). Escola Politécnica da Universidade de São Paulo. São Paulo, MARTINS, R. A. Conceitos básicos de controle estatístico da qualidade. EDUFSCar. São Carlos, MONTGOMERY, D. C. Introdução ao Controle Estatístico de Qualidade. 4ª ed. LTC. Rio de Janeiro,

14 OLIVEIRA, T. S. Aplicação do controle estatístico de processo na mensuração da variabilidade em uma usina de etanol. ENEGEP. São Carlos, ÚNICA. Ethanol: EPA reaffirms sugarcane biofuel is advanced Renewable fuel with 61% less emissions than gasoline. News, 02 mar Disponível em: < Acesso em: 13 jan