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1 THE APPLICATION OF STATISTICAL QUALITY CONTROL TOOLS TO MONITORING ETHANOL PROCESS PRODUCTION. Juliana Keiko Sagawa (UFSCAR ) [email protected] Ricardo Inoue Yamada (UFSCAR ) [email protected] 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. XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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1

THE APPLICATION OF STATISTICAL

QUALITY CONTROL TOOLS TO

MONITORING ETHANOL PROCESS

PRODUCTION.

Juliana Keiko Sagawa (UFSCAR )

[email protected]

Ricardo Inoue Yamada (UFSCAR )

[email protected]

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.

XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

2

1.1

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 2050. 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.

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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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.

3.1. 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.

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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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.

3.2. 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.

3.3. Process-capability analysis

According Montgomery (2004), the magnitude of CpK index is a direct measure of how off-

center 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.

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

5

CpK Index Classification Interpretation

CpK < 1,00 Unstable Process The capacity of the process is inappropriate

to the required specification.

1,00 < Cpk < 1,33 Partially able Process The capacity of the process is within the

required specification.

Cpk ≥ 1,33 Stable Process The capacity of the process is adequate to

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.

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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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.

4.1. 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

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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.

4.2. 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

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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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.

4.3. 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

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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:

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

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Temperature in the fermentation vat (ºC)

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.

Analysis of Cpk Histogram observations

Period 1:Unstable process (0.77)

Period 2: Stable process (1.59)

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 Histogram observations

Period 1: Unstable process (0.11)

Period 2: Unstable process (0.39)

Distribution with moderate variability and off-center.

Brix of the mash (ºBrix)

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.

Analysis of Cpk Histogram observations

Period 1: Unstable process (0.57)

Period 2: Partially able process (1.11)

Distribution with low variability and somewhat

off-center.

Temperature of the mash (ºC)

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

Salvador, BA, Brasil, 08 a 11 de outubro de 2013.

11

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.

Analysis of Cpk Histogram observations

Period 1: Unstable process (0.40)

Period 2: Unstable process (0.14)

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 Histogram observations

Period 1: Unstable process (0.95)

Period 2: Unstable process (0.43)

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 Histogram observations

Period 1: Unstable process (-0.87)

Period 2: Unstable process (-0.52)

Distribution with high variability and significantly

off-center.

Infection in the fermentation vats (x107)

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XXXIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO A Gestão dos Processos de Produção e as Parcerias Globais para o Desenvolvimento Sustentável dos Sistemas Produtivos

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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 Histogram observations

Period 1: Unstable process (-0.15)

Period 2: Unstable process (-0.10)

Distribution with high variability and significantly off-

center.

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

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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, 2005.

IPCC. Renewable Energy Sources and Climate Change Mitigation Cambridge University Press,

Cambridge, United Kingdom, 2011. 1075 p. Disponível em: <http://www.ipcc.ch/publications_and_data/

publications_and_data_reports.shtml#SRREN > Acesso em: 11 jan. 2013.

LIMA, U. A.; BASSO, L. C.; AMORIM, H. V. Produção de Etanol. In: LIMA, U. A. et al.

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