Social, Economic and Environmental Metrics for the Sustainable OPTIMIZATION of Chemical and Petroleum Processes

This research is focused on investigating sustainability metrics for the optimization of chemical and petroleum processes. Traditionally, engineers designed processes to meet economic goals; however with the awareness of sustainability, engineers are now considering other constraints such as resource usage, environmental impacts, social benefits and economics. Several metrics and indicators for quantifying sustainability exist; yet, there is no straight forward computer aided tool and path that engineers can follow in order to design for sustainability. Investigating and validating available indexes and metrics and implementing the three major sustainability criteria into process design and optimization are the goals of this research. A well defined methodology that will include process optimization under social, economic and environmental constraints is being developed. This involves building upon existing multi-objective algorithms that handle selected sustainability criteria and constraints.

This research will contribute to sustainability development in chemical engineering as benefits such as reduced pollution minimize resource usage and more economic products will be thoroughly explored. Also, this tool will assist in reducing the negative impact to the ecosystem as this methodology will be applicable other processes industries such as chemical, petroleum and food. Therefore it will benefit the society in the long run through the development of more economic and environmentally viable processes.

This poster will introduce our methodology as well as present results from evaluating the Green Metric system and our waste minimization procedure. The waste minimization procedure involves a simultaneous approach to maximize profit, while minimizing waste through source reduction with the implementation of multi objective optimization with process simulation. To accomplish this objective, the acrylonitrile process was studied. The approach involved four steps: process modeling and analysis, identification of process alternatives, selection of process alternatives and incorporation of multiobjective optimization. The simulator ASPEN PLUS? version 12.1 was used to model and evaluate the process. Process alternatives were identified from both literature and sensitivity analysis. Multiobjective optimization helps initially in quickly eliminating inferior alternatives and then in enhancing the superior alternative to develop an efficient process. Through this work, a general methodology was developed for the application of process optimization to process design. Using the concepts of multiobjective optimization, a sensitivity curve that shows the effect of waste treatment on the savings for the process can be developed. Based on the sensitivity analysis results, the Plug Flow reactor scheme was the better alternative. After process modification, the process can earn a maximum net savings of $ 2.64 x 107 in seven years, an increase of 69% over the base case. At the other end of the spectrum, the modified process could generate waste of 4153 lb/hr, a 38% reduction compared to the base case.