Enzymatic transesterification of triglycerides in a continuous way is always a great challenge with a large field of applications for biodiesel, bio-lubricant, bio-surfactant, etc. productions. The lipase B from Candida antarctica (CalB) is the most appreciated enzyme because of its high activity and its non-regio-selectivity toward positions of fatty acid residues on glycerol backbone of triglycerides. Nevertheless, in the field of heterogeneous catalysis, we demonstrated that the medium hydrophilic nature of the support used for its commercial form (Lewatit VPOC1600) is a limitation. Glycerol is adsorbed onto support inducing drastic decrease in enzyme activity. Glycerol would form a hydrophilic layer around the enzyme resulting in diffusional limitations during triglyceride transfer to the enzyme. Accurel MP, a very hydrophobic macroporous polymer of propylene, was found not to adsorb glycerol. Immobilization conditions using this support were optimized. The best support was Accurel MP1001 (particle size<1000 μm) and a pre-treatment of the support with acetone instead of ethanol enables the adsorption rate and the immobilized enzyme quantity to be maximized. An economical approach (maximization of the process net present value) was expanded in order to explore the impact of immobilization on development of an industrial packed bed reactor. The crucial ratio between the quantity of lipase and the quantity of support, taking into account enzyme, support and equipped packed bed reactor costs was optimized in this sense. The biocatalyst cost was found as largely the main cost centre (2-10 times higher than the investments for the reactor vessel). In consequence, optimal conditions for immobilization were a compromise between this immobilization yield (90% of lipase immobilized), biocatalyst activity, reactor volume and total investments.

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.