Multi-objective modeling, simulation and optimization for economically and environmentally conscious decision making

The decision making is a process flow from the cognition of information to the optimal selection among more than one alternative possibility. It is one of great significant and central activities in the business and manufacturing management of chemical industry. The reasons for the need of economically and environmentally conscious decision making can be observed in a lot of fields. In this dissertation, several applications will be introduced through the presentation of developed methodologies and case studies.;It is very important to apply modeling, simulation, and optimization to provide economically and environmentally conscious decision making and valuable quantitative engineering supports for multiple stake holders, including government environmental agency, regional chemical plants, and local communities. In this dissertation, the general introduction will be given for our previously developed three optimization models with the systematic mathematical simulation and the methodology of multi-objective optimization algorithm. The first model aims to detect the possible emission sources (chemical plants) and identify the abnormal emission profile (emission source location, starting time, time duration, dynamic emission rate and total emission amount) from an accidental emission source responsible for an observed emission event based on an available air-quality-monitoring network so as to support diagnostic and prognostic decisions in a timely and effective manner. It provides valuable information for investigations of accidents and root-cause analysis for emission events; meanwhile, it helps evaluate the regional air-quality impact caused by such emission events as well. The second model will conduct the multi-plant start-up emission evaluation and help to provide control strategy. For any air quality violation is predicted to an AQCR, a multi-objective scheduling problem will be generated and solved to optimize the start-up sequence and start-up beginning time for all chemical plants. The scheduling model minimizes the overall air quality impacts to all of the AQCRs and the total start-up time mismatch of all plants, subject to the principles of atmospheric pollutant dispersion.;The third developed mathematical model help to set up the quantitative relationship among the regional background air-quality information, new plant emissions, and local statistical meteorological conditions. The simultaneous consideration for all these factors will support the risk assessment for the thorough evaluation of the potential air-quality impacts from new chemical plant site selection. The optimization will be further proceeded to determine the final site with the minimal air-quality impacts. The fourth model evaluates a super water reuse network design (WRND) with the integrated structure for multiple WRNDs in order to satisfy the requirement of manufacturing multiple products according to their processing recopies. The optimization of dynamic status transitions between any two WRNDs are achieved by our proposed dynamic control strategy. There will be two objectives which are targeting to minimize the dynamic transition time from one WRND to another and minimize the mass quantity (freshwater, manipulation chemical, wastewater) during such transitions. It will help to handle the problems of dynamic shift operations among multiple production recipes. (Abstract shortened by UMI.).