Multi-Objective Optimization of Solid State Fermentation Process

Solid state fermentation is among the key processes to produce enzymes and which can serve various purposes in the food and agricultural industries, etc. Modeling of bioreactors also plays an important role in understanding the bio process, design, and development of process. The essential parameters best suited for a particular case of enzyme production were recognized in this work. The simulated mathematical model predicts the production of protease enzyme by Aspergillus niger under various operating conditions and values of parameters. Evolutionary multi-objective optimization (MOO), in this study, is used for MOO of the solid state fermentation process considering two case studies of two objectives (maximization of enzyme activity versus minimization of fermentation time and maximization of product to cell yield coefficient versus minimization of fermentation time) and variables (air flow rates, air temperature, moisture content, parameters for cooling and pressure). This paper presents the resulting optimal Pareto front and the possible effects of individual parameters on multiple objectives. To serve the same purpose, simulation runs were taken at different heights of bioreactor so as to foresee the effects of scale-up on the performance of bioreactor – and henceforth the process.

[1]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[2]  Kishalay Mitra,et al.  Handling Uncertainty in Kinetic Parameters in Optimal Operation of a Polymerization Reactor , 2011 .

[3]  D. Mitchell,et al.  Mathematical modeling as a tool to investigate the design and operation of the zymotis packed-bed bioreactor for solid-state fermentation. , 2000, Biotechnology and bioengineering.

[4]  C. Hesseltine,et al.  Biotechnology report: Solid state fermentations , 1972, Biotechnology and bioengineering.

[5]  G. Berrozpe,et al.  Chromosome abnormalities in peripheral blood lymphocytes from untreated Hodgkin's patients , 1988, Human Genetics.

[6]  N. Karanth,et al.  Interaction of transport resistances with biochemical reaction in packed-bed solid-state fermentors: effect of temperature gradients. , 1994, Enzyme and microbial technology.

[7]  P. Greenfield,et al.  A packed bed solid-state cultivation system for the production of animal feed: Cultivation, drying and product quality , 1992, Biotechnology Letters.

[8]  G. P. Rangaiah,et al.  Economic and Environmental Criteria and Trade-Offs for Recovery Processes , 2011 .

[9]  M. Raimbault,et al.  Water and water activity in the solid state fermentation of cassava starch by Aspergillus niger , 1988, Applied Microbiology and Biotechnology.

[10]  C. Soccol,et al.  New developments in solid state fermentation: I-bioprocesses and products. , 2000 .

[11]  Deidre Mary Stuart,et al.  Solid-state fermentation in rotating drum bioreactors , 1996 .

[12]  M. Raimbault,et al.  Culture method to study fungal growth in solid fermentation , 1980, European journal of applied microbiology and biotechnology.

[13]  B. V. Babu,et al.  Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor , 2010, Int. J. Bio Inspired Comput..

[14]  Nadia Krieger,et al.  Thermal denaturation: is solid-state fermentation really a good technology for the production of enzymes? , 2004, Bioresource technology.

[15]  B. V. Babu,et al.  Modified differential evolution (MDE) for optimization of non-linear chemical processes , 2006, Comput. Chem. Eng..

[16]  Yiming Li,et al.  Hybrid Differential Evolution and Particle Swarm Optimization Approach to Surface-Potential-Based Model Parameter Extraction for Nanoscale MOSFETs , 2011 .

[17]  Chandrasekharan Rajendran,et al.  A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs , 2005, Eur. J. Oper. Res..

[18]  J Tramper,et al.  Temperature control in a continuously mixed bioreactor for solid-state fermentation. , 2001, Biotechnology and bioengineering.

[19]  B. V. Babu,et al.  Optimization of Adiabatic Styrene Reactor: A Hybrid Multiobjective Differential Evolution (H-MODE) Approach , 2009 .

[20]  G. P. Rangaiah,et al.  Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations , 2006 .

[21]  G. Viniegra-González,et al.  Heat transfer simulation in solid substrate fermentation , 1990, Biotechnology and bioengineering.

[22]  Surendra Kumar,et al.  Modelling of a packed bed solid-state fermentation bioreactor using the N-tanks in series approach , 2007 .

[23]  B. Babu,et al.  Multiobjective Optimization of Industrial Processes Using Elitist Multiobjective Differential Evolution (Elitist-MODE) , 2011 .

[24]  David A. Mitchell,et al.  Incorporation of death kinetics into a 2-dimensional dynamic heat transfer model for solid state fermentation , 1995 .

[25]  Nirupam Chakraborti,et al.  Multiobjective Optimization of Manganese Recovery from Sea Nodules Using Genetic Algorithms , 2008 .

[26]  Ashish M. Gujarathi,et al.  Improved Multiobjective Differential Evolution (MODE) Approach for Purified Terephthalic Acid (PTA) Oxidation Process , 2009 .

[27]  D. M. Griffin,et al.  Water and Microbial Stress , 1981 .

[28]  B. Babu,et al.  Optimization of process synthesis and design problems: A modified differential evolution approach , 2006 .

[29]  B. V. Babu,et al.  Multi-objective optimization of industrial styrene reactor: Adiabatic and pseudo-isothermal operation , 2010 .

[30]  B. K. Lonsane,et al.  Potential of solid state fermentation for production of ergot alkaloids , 1992, Letters in applied microbiology.

[31]  N. Krieger,et al.  New Developments in Solid-State Fermentation. Part 2. Rational Approaches to the Design, Operation and Scale-up of Bioreactors , 2000 .

[32]  Nirupam Chakraborti,et al.  Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms , 2009 .