Optimization of Operating Conditions for Protein Production Plants using Mixed Integer Nonlinear Programming and Genetic Algorithms
Optimization of Operating Conditions for Protein Production Plants using Mixed Integer Nonlinear Programming and Genetic Algorithms
This work deals with the problem of the search for optimal investment cost of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using Genetic Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four point crossover operator, which take into account, the uncertainty on the demand using Gaussian process modeling. Experiments indicated that relatively good results could be obtained using 4-point crossover with an applied rate of 0.7 promised to give best performance. The results (number and size of equipment, investment cost, production time (Hi), CPU time and Idle times in plant) obtained by GAs are the best.
In chemical engineering, there has been an increased interest in the development of systematic method for the design of batch process in specialty chemicals, food products, and pharmaceutical industries. Most processes in the modern biotechnology industry correspond to batch plants and with the rapid development of new products (i.e. both therapeutic and non-therapeutic proteins). The main host for recombinant proteins for many years has been Escherichia coli. However, the developments with yeast cells have grown at a very rapid pace, which has resulted in several important commercial products such as insulin, hepatitis B vaccine, and also more recently, chymosin and protease. The fact that many recombinant proteins made in yeast can be made to be secreted out of the cell and that yeast allows for at least partial glycosilation is an added bonus for this host, therefore, in the optimal design of a multiproduct batch chemical process, the production requirement of each product and the total production time available for all products are specified. The number and size of parallel equipment units in each stage as well as the location and size of intermediate storage are to be determined in order to minimize the investment cost. The common approach used by previous research in solving the design problem of batch plant has been to formulate it as a Mixed Integer Nonlinear Programming (MINLP) problem and then employ optimization techniques to solve it. Robinson and Loonkar studied the problem of designing multiproduct plants operating in single product campaign mode and with a single unit in each processing stage and they extended the nonlinear programming model to include both the design of discrete equipment size and the selection of the parallel units number, by solving it through the use of heuristics and branch and bound. The same problem was further formulated by Grossmann and Sargent as a (MINLP) model. Knopf et al. and Yeh et al. accounted for the presence of semicontinuous units. Voudouris and Grossmann proposed reformulations of the previous design models where discrete sizes are explicitly accounted for.
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