(426d) Uncertainty Issues in Multi-Objective Optimization of Industrial Waste and Energy Management in Chemical Sites

Authors: 
Capon, E. - Presenter, Universitat Politecnica de Catalunya
Papadokonstantakis, S., Swiss Federal Institute of Technology, Zurich (ETHZ)
Hungerbühler, K., Swiss Federal Institute of Technology, Zurich (ETHZ)



Waste liquid effluents originated from industrial activities usually contain contaminants and certain species such as organic solvents, whose treatment involves added value for their energetic content. Therefore, not only does industrial waste management stand for an end-of-pipe problem, but also for a source of energy generation, which can result in significant benefits in the site if energy integration strategies are adopted.

In practice, treatment allocation decisions are usually taken based on company-specific selection criteria, since the choice of the adequate option for an entire manufacturing site with hundreds of ever changing effluents becomes an overwhelming task. In the literature, this problem has been tackled as a design problem of wastewater treatment network, which is part of the water network problem involving mass exchange concepts. In this area, certain simplifications should be removed to reach more practical applications (Jezowski, 2008), for example rigorous process models, schedule optimization, heat integration or storage policies should be considered.

An extensive and specific work related to industrial waste treatment management has been presented by Chakraborty et al. (2002, 2003, 2003a). The work combines informed search for systematical synthesis for structural alternatives with rigorous mathematical programming for selecting the optimal flowsheet and its operating parameters. They further extend their work to include uncertain parameters, and demonstrate how to develop and update long-term site wide waste reduction efforts and investment strategies under uncertainty. The importance of considering uncertainty is highly justified along the work.

Therefore, this work aims at providing a systematic approach to optimize industrial waste treatment scheduling considering treatment unit models and waste mixing policies under economic and environmental objectives along with the energetic integration under uncertainty issues. The specific behavior of the considered waste management treatments is included in the optimization problem as black-box models based on practical industrial practice. To achieve more realistic solutions, the estimation of waste treatment costs and environmental impacts has been explicitly added to the assessment scheme, as well as the constraints of the operating conditions in the treatment units and the fulfillment of environmental regulations for water and air emissions.  This framework is applied to an industrial based case study and used to analyze the waste mixing potential, considering an uncertainty about 20% in the total income flow to the waste treatment facilities. A rigorous multi-objective mathematical strategy is proposed and compared in terms of solution quality and computational complexity to a previously problem approach based on meta-heuristics. In addition, a sequential approach is adopted to further estimate the minimum heat requirements for the different solutions obtained in the Pareto front using a MILP formulation of the heat exchange problem.

References

 Chakraborty, A., Linninger, A. A., 2002. Plant-wide waste management. 1. Synthesis and multiobjective design. Industrial & Engineering Chemistry Research 41, 4591-4604.

Chakraborty, A., Linninger, A. A.,  2003. Plant-wide waste management. 2. decision making under uncertainty. Industrial & Engineering Chemistry Research 42 (2), 357-369.

Chakraborty, A., Colberg, R. D., Linninger, A. A., 2003a. Plant-wide waste management. 3. long-term operation and investment planning under uncertainty. Industrial & Engineering Chemistry Research 42 (20), 4772-4788.

Jezowski, J., 2008. Review and analysis of approaches for designing optimum industrial water networks. Chemical and Process Engineering-inzynieria Chemiczna I Procesowa 29 (3), 663-681.

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