(538d) Simulation-Based Optimization Framework With Heat Integration | AIChE

(538d) Simulation-Based Optimization Framework With Heat Integration


Chen, Y. - Presenter, Carnegie Mellon University
Eslick, J., National Energy Technology Laboratory
Grossmann, I., Carnegie Mellon University
Miller, D. C., National Energy Technology Laboratory

Simulation-based optimization framework with heat integration is an important tool for the optimal design of power and chemical plants. It utilizes well-developed rigorous simulation models for the process to obtain accurate results and is able to perform heat integration for the existing feasible designs.

In this optimization framework, process simulators, a heat integration module are incorporated with a derivative free optimization (DFO) module to obtain the best economic performance of a process. The optimization approach involves using a process simulator to calculate production information (e.g., raw material consumption rates, production rates and equipment sizes) from process inputs (including initial inputs and intermediate results from the optimizer). The heat integration module receives information of the units (e.g., heating and cooling requirements) and process streams (e.g., flow rates, temperatures, enthalpies) from the results of the process simulator. It determines the minimum utility cost (or utility consumption) and minimum number of heat exchangers, which is sent to the DFO module, which minimizes the total cost incorporating results from both the process simulation and the heat integration module.

Multiple software tools are involved in the above framework. For example, process simulations are performed in Aspen Plus, Aspen Custom Modeler (ACM), gPROMS, etc., while the heat integration problem is solved using GAMS. An efficient linkage framework is developed in Python, which achieves fast data transfer between different software. The entire optimization framework, including the process simulation and heat integration, is controlled via a graphic user interface (GUI) for the ease of implementation. This optimization framework was demonstrated for the integration of a carbon capture and compression system with a fossil energy power plant. The results indicate that both the net efficiency and gross power output of the plant can be significantly increased after optimization and heat integration.