FUEL ETHANOL FROM BRAZILIAN BIOMASS: ECONOMIC .estocástica utilizando planilha eletrônica baseada

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FUEL ETHANOL FROM BRAZILIAN BIOMASS: ECONOMIC RISKANALYSIS BASED ON MONTE CARLO SIMULATION

TECHNIQUES

Nvaes1, W. S.; Caixeta, S. H. M; Selvam, P. V. P.; Santos, H. P. A.; Costa, G. B.Universidade Federal do Rio Grande Norte UFRN, Ncleo de Tecnologia NT

Programa de Ps Graduao em Engenharia Qumica PPGEQGrupo de Pesquisa em Engenharia de Custos e Processos GPEC

Campus Universitrio Lagoa Nova, CEP: 59072 970 Natal/RNHome Page: www.ufrngpec.hpg.com.br; E Mail: alquimista@eq.ufrn.br1

1 Author to whom correspondence should be addressed.

ABSTRACT

At the present the fuel ethanol fromBrazilian biomass is one of the most favorableenergetic project in development to the Brazilmainly with respect to the rural employment,environment and energetic safety. In thiscontext, a technological and economical studywas developed applied to fuel ethanolproduction from sugar cane based on dynamicmodeling, simulations and economical riskanalysis. The objective of this work concern todevelop an stochastic methodology and as wellas the implementation of the economic riskanalysis inherent to the ethanol processproduction by the changing of the processvariables and parameters based on MonteCarlo simulation method. The mass balanceand economic evaluation was obtained fromsimulations were carried out using theSuperPro DesigneTM v. 3.0 software. Inaddition to this, the economic risk analysis wascarried out based on changing in thedistribution of the selling price, yield offermentation and feed flow rate the processstochastic variables. The stochastic analysisusing Monte Carlo simulation wasimplemented in a spreadsheet model utilizing@Risk v. 4.5 software for Excel. Based ondeterministic and stochastic simulations of thefuel ethanol production the results saveraltechn economical parameters have beenobtained and analyzed. The importantadvantage of the proposed method is thepossibility to predict the economical risk

involved in the ethanol production take in toaccount the aleatory and complex natureinherent to the process, with precision,reliability and very rapidly.KEY WORDS: Ethanol, Simulation,Stochastic, Monte Carlo, Risk Analysis.

RESUMO

O etanol derivado da biomassa brasileiraconstitui hoje um dos projetos energticos maispropcios em desenvolvimento para o Brasil,principalmente no que diz respeito ao empregorural, ao meio ambiente e a segurana energtica.Pensando nisso, desenvolvemos um estudotecnolgico e econmico sobre a produo deetanol derivado da cana de acar, utilizandomodelagem dinmica, simulao e anlise de riscoeconmico atravs de mtodos estocsticos. Oobjetivo do presente trabalho consiste emdesenvolver um modelo estocstico para asimulao e tambm a implementao da anlise derisco econmico envolvido no projeto, devido variao de parmetros e variveis de processoatravs do mtodo Monte Carlo de simulaoestocstica. O balano de massa e a avaliaoeconmica foram obtidos atravs de simulaes,utilizando para isso o software SuperProTM

Designer v.3.0. Realizou-se, tambm, a anlise derisco econmico variando o preo de venda,rendimento de fermentao e a capacidade deproduo, consideradas como variveis estocsticasdo processo. Desenvolvemos a simulaoestocstica utilizando planilha eletrnica baseadaem mtodos de distribuio de incerteza e osoftware @Risk, simulador Monte Carlo. Atravsda simulao do processo de produo de etanolcombustvel, foram obtidos como resultados os

investimentos necessrios, os lucros e o retornosobre os investimentos. Pelos resultados obtidos,podemos concluir que por meio de nossassimulaes e anlise de tais resultados, que atravsdo mtodo Monte Carlo possvel realizar umamodelagem considerando a natureza aleatria ecomplexa inerente a cada processo qumico, comrapidez, preciso e confiabilidade dos resultadosobtidos.

PALAVRAS CHAVES: Etanol combustvel,Simulao, Estocstica, Monte Carlo, Anlise deRisco.

INTRODUCTION

The increasing capability of computerprocessing has facilitated the development and useof software to simulate chemical and othersindustrial process. The procedure of processsimulation consist in the resolution of the set ofmathematical relations that describe systembehaviour, for example, mass and energy balance,phase equilibrium equations, physical propertiescalculations, etc. Where as such mathematicalmodel contains input variables, process parametersand output variables. The traditional simulationapproach involve definition on input variables andprocess parameters, thus output variables areevaluated by use of the model, which producesdeterministic (point estimate) results for umparticular set of assumptions. (MIZUTANI et al,2000)

Fuel ethanol production industries areusually faced with uncertain conditions during theiroperation. These uncertain can arise from variationseither in external parameters, such as the quantityof feed streams, temperature and concentration, orinternal process parameters such transfercoefficients, reactions constants, physical propertiesand as well as price of fuel ethanol because ofvariation in fuel price market. If the technology isnew, there are additional uncertainties due tolimited performance data. Thus, utilisation of thedeterministic simulation results can be limited dueto the uncertainties associated with the variablesand parameters in the real process and the solutionof the model can be significant from the realprocess response. Therefore, an efficient andsystematic method is required to evaluateuncertainties and investigate the effect of theuncertainties in chemical process simulation.

An approach tool widely used foruncertainty analysis employs the concept ofstochastic analysis simulation (DIWEKAR andRUBIN, 1991). This approach is based on a largenumber of simulation runs, in each run, inputvariables and process parameters are randomlyselected according to adequate probability densityfunctions. The set of out put variables evaluated

gives important information about the behaviour ofthe statistical process, indicating the most probablevariable range.

In short, the process analysis of realsystems requires both stochastic and deterministicmodelling capabilities. Yet, not much works havebeen realised in Brazil about stochastic modellingapplied to fuel ethanol production even thoughBrazil is the leader in this field.

Thus, the objective of this paper is todevelop a generalised stochastic modellingmethodology applied to uncertainties analysisinherent to the fuel ethanol process productionaided for deterministic simulator and stochasticspreadsheet models based on Monte Carlosimulation techniques.

DETERMINISTIC ANALYSIS

The modelling tools in current simulatorsmay roughly classified into two groups, block-oriented (or modular) and equations-orientedapproaches. (MARQUARDT, 1996)

Block-oriented approach mainly addressesmodelling on the flowsheet level. Every process isabstracted by a block diagram consisting ofstandardised blocks, which model behaviour of aprocess unit or part of it. All the blocks are linkedby single-like connections representing the flow ofinformation, materials and energy employingstandardised interface and stream formats.

Models of process unit are preceded byone model expert and incorporated in a modellibrary for later use. Modelling on the flowsheetlevel is either supported by one modelling languageor by a graphical editor. In both cases, the end userselect the models from the library, provides themodel parameters and connects them to the plantmodel. The incorporated chemical engineeringknowledge as well as the model structure arelargely fixed and not accessible. Commonexceptions are physical property models, which canbe selected independently of the process unitmodel.

Equation-oriented modelling tool supportthe implementation of unit models and theirincorporation in a model library by means ofdeclarative modelling language or by providing aset of subroutine templates to be complete directoryin a procedural programming language. There areno different tools for modelling expert or for theend user. Hence, modelling on the unit levelrequires profound knowledge in such diverse areasas chemical engineering, modelling and simulation,numerical mathematics, and computer science. Thedevelopment of novel process unit models istherefore often restrict to a small group of expert.

Theses later approaches do not manyadvantage compared to the modular modelling toolmeanly in the implementation complexity because

the equation-modular approaches requires a highcomputational and technical knowledge.

The SuperPro DesignerTM v.3.0 softwarehas been the computational tool used in this study.The SuperPro Designer is part of the block-oriented simulator group. It is comprehensivecomputer simulations package the fate of chemicalsin individual and combinations of unit operation. Alist of these chemicals that is global to the processflowsheet is specified from an extensive databaseprovided with the software. User-defined chemicalscan be added of this database. The process diagramis built in a point and click CAD environment byadding unit operation to a flowsheet andestablishing the connectivity between these unitsusing streams. The chemical components of thestreams that serve as influent to the whole processare defined. (INTELLIGEN, 1994 and 1998)

The consecutive unit operations are solvedusing sequential mass balances where the effluentstreams from one unit operation serves as theinfluent streams to another unit operation.(INTELLIGEN, 1994 and 1998)

Results of the simulation can be viewed byclicking directly on stream or unit operation on thescreen. Comprehensive output can be obtained bygenerating stream and input data report that can beviewed in separating windows. (INTELLIGEN,1994 and 1998)

Beyond mass balan