Methods and algorithms for integrated multi-scale optimisation of production planning and scheduling

Imperial Users onl

A hybrid algorithm for the integrated production planning in the pulp and paper industry

Tese de mestrado integrado. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Optimal planning and feedstock-mix selection for multiproduct polymer production

In this paper, we describe a nonlinear programming model to determine the optimal balance of feedstocks to manufacture multiple polymer grades in a polypropylene production facility. The main units of the process are a distillation column and a polymerization reactor, for which accurate short-cut process models were developed. Both a single and multiple product formulations are presented. The proposed models seek to maximize the plant throughput while minimizing the production costs. The possibility of adding extra production is also considered. The formulations are applied to several case studies, both to analyze the performance of the model and to illustrate its potential economic impact. The trade-off between feedstocks costs and production rates is analyzed by solving the multiple-product model with different time horizons. An annualized-slate long term case study is presented. The proposed formulation with a user-friendly interface has been deployed to assist with commercial and operation decisions at the plant.The authors would like to acknowledge the financial support received from Braskem America and the Center for Advanced Process Decision Making (CAPD)

Stochastic Optimization of Bioreactor Control Policies Using a Markov Decision Process Model

Biopharmaceuticals are the fastest-growing segment of the pharmaceutical industry. Their manufacture is complicated by the uncertainty exhibited therein. Scholars have studied the planning and operation of such production systems under some uncertainties, but the simultaneous consideration of fermentation and resin yield uncertainty is lacking so-far. To study the optimal operation of biopharmaceutical production and purification systems under these uncertainties, a stochastic, dynamic approach is necessary. This thesis provides such a model by extending an existing discrete state-space, infinite horizon Markov decision process model of upstream fermentation. Tissue Plasminogen Activator fermentation and chromatography was implemented. This example was used to discuss the optimal policy for operating different fermentation setups. The average per-cycle operating profit of a serial setup was 1,272 $; the parallel setup produced negative average rewards. Managerial insights were derived from a comparison to a basic, titer maximizing policy and process sensitivities. In conclusion, the integrated stochastic optimization of biopharma production and purification control aids decision making. However, the model assumptions pose room for further studies. Keywords: Markov decision process; biopharmaceuticals production; fermentation uncertainty; chromatography resin; stochastic performance decay