Artur M. Schweidtmann

A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes.

A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions.

Deterministic Global Optimization with Artificial Neural Networks Embedded

Artificial neural networks are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of optimization problems with artificial neural networks embedded. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced space (Mitsos et al. in SIAM J Optim 20(2):573–601, 2009) employing the convex and concave envelopes of the nonlinear activation function. The optimization problem is solved using our in-house deterministic global solver. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process. The results show that computational solution time is favorable compared to a state-of-the-art global general-purpose optimization solver.