Abstract

The bioprocesses, meaning the transformation of raw materials by either free or attached enzymes produced by living matter or by microorganisms themselves, were a technological constant presence in human history. Up to the present days, the bioprocesses were designed and operated with the aim of achieving some economic, environmental, and social criteria, while sustainability was only another assessment of the aforementioned criteria. In the context of the present knowledge in the domain, sustainability-based optimization would be the next ideal step towards the improvement of the economical, environmental and social parameters of bioprocesses. In the frame of this work, sustainability will be treated as an optimization task where the objective function(s) is/are either a performance index or a set of performance indices, subject to economic, environmental and social restrictions. The aforementioned original multi-level optimization approach refers to two aspects: first, the hierarchic top-down procedure, from plant to process unit scales; second, a combined interaction of manpower and plant networks. For each level and/or network, several objective functions will be derived from sustainability concepts, together with the subjacent penalties. The optimization process will use either an averaged objective function (the sum of the weighted objective functions on to the same level), or a vector objective function aiming to emphasize the Pareto front in the space of feasible solutions. The models of bioprocesses used in the optimization belong to one of these two categories: a) the first principle models encoded into the SuperPro Designer™ simulator; b) original models, of theoretical nature and/or based on artificial neural networks (ANN). An attempt to quantify some qualitative ecological and social sustainability indicators (in connection with the specificity of the test cases) will be made. Due to the incertitude that is present in both bioprocesses modeling, and evaluation of sustainability indicators, some studies involving informational entropy, sensitivity analysis and Monte Carlo simulations must be done.