(211g) Optimal Design and Operation of Hybrid CO2 Capture Systems
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Advances in Fossil Energy R&D
Design and Optimization of Environmentally Sustainable Advanced Fossil Energy Systems
Monday, November 14, 2016 - 5:09pm to 5:28pm
This work presents a mixed integer nonlinear programming model (MINLP) for the optimal design and operation of carbon capture plants. A mix of first principle and surrogate-based models have been developed to characterize the process design and operation.
Coal-based power generation is one of the most important sources of energy in the US. As such, the importance of cost effective carbon capture and storage (CCS) technologies to mitigate the environmental issues of power generation plants is obvious. The most significant technologies for carbon capture include solvents, membrane and solid sorbent systems. This work analyzes and compares the solid adsorption and membrane systems on the basis of minimizing the cost of electricity.
In solid sorbent capture systems the process is mainly driven by the interaction of the solid sorbent particles with the flue gas (mainly consisting of N2, CO2, and H2O). The main process units of the solid sorbent systems are multiphase reactors, such as moving bed and fluidized beds.
In gas permeation separation systems, the CO2 is separated by high CO2 selective membranes. The main driving force of the process is the partial pressure between the feed and the permeate side. A typical membrane separation plant consists of multi-stage membrane configurations and compressors.
Given the modeling complexity and the difficulty of formulating algebraic models for such processes, previous studies are generally limited to either process-simulation1 based frameworks or simpler models for mathematical optimization. This work proposes a mathematical optimization framework in which both first principles-based models and surrogate models2 have been developed to obtain the optimal plant design and operation to capture at least 90% of the CO2. The use of commercial process simulators to develop detailed, carefully tuned models have been exploited in the preparation and characterization of surrogate models (ALAMO2). Model validation and sample generation play a very important role in this mathematical framework.
The modeling framework considers a set of adsorbers (with two possible operating technologies), regenerators (with two possible operating technologies), membrane stages, mixers, splitters, compressors, pumps and heat exchanger units which can be installed or not. Design decisions determine the plant layout, by selecting the optimal number of parallel trains, number of adsorbers and regenerators, number of membranes, equipment design (diameter, length, etc.), heat exchangers, mixers, splitters and configuration of the system. At the operating level, the decisions involve the optimal flows, temperatures, concentrations of gas and solids. For the adsorbers and regenerators, the solids bed length, flux velocity and other parameters are also considered. While, the membrane characteristics are to be determined by the permeability, permeate fraction, pressure ratio, among others.
[1] Eslick, J. C., Tong C., Lee A., Dowling A. W., Mebane D. S. (2015). Optimization under uncertainty with rigorous process models. Proceedings AIChE Annual Meeting 2015.
[2] Cozad, A., N. V. Sahinidis and D. C. Miller, Learning surrogate models for simulation-based optimization, AIChE Journal, 2211-2227, 2014.
Coal-based power generation is one of the most important sources of energy in the US. As such, the importance of cost effective carbon capture and storage (CCS) technologies to mitigate the environmental issues of power generation plants is obvious. The most significant technologies for carbon capture include solvents, membrane and solid sorbent systems. This work analyzes and compares the solid adsorption and membrane systems on the basis of minimizing the cost of electricity.
In solid sorbent capture systems the process is mainly driven by the interaction of the solid sorbent particles with the flue gas (mainly consisting of N2, CO2, and H2O). The main process units of the solid sorbent systems are multiphase reactors, such as moving bed and fluidized beds.
In gas permeation separation systems, the CO2 is separated by high CO2 selective membranes. The main driving force of the process is the partial pressure between the feed and the permeate side. A typical membrane separation plant consists of multi-stage membrane configurations and compressors.
Given the modeling complexity and the difficulty of formulating algebraic models for such processes, previous studies are generally limited to either process-simulation1 based frameworks or simpler models for mathematical optimization. This work proposes a mathematical optimization framework in which both first principles-based models and surrogate models2 have been developed to obtain the optimal plant design and operation to capture at least 90% of the CO2. The use of commercial process simulators to develop detailed, carefully tuned models have been exploited in the preparation and characterization of surrogate models (ALAMO2). Model validation and sample generation play a very important role in this mathematical framework.
The modeling framework considers a set of adsorbers (with two possible operating technologies), regenerators (with two possible operating technologies), membrane stages, mixers, splitters, compressors, pumps and heat exchanger units which can be installed or not. Design decisions determine the plant layout, by selecting the optimal number of parallel trains, number of adsorbers and regenerators, number of membranes, equipment design (diameter, length, etc.), heat exchangers, mixers, splitters and configuration of the system. At the operating level, the decisions involve the optimal flows, temperatures, concentrations of gas and solids. For the adsorbers and regenerators, the solids bed length, flux velocity and other parameters are also considered. While, the membrane characteristics are to be determined by the permeability, permeate fraction, pressure ratio, among others.
[1] Eslick, J. C., Tong C., Lee A., Dowling A. W., Mebane D. S. (2015). Optimization under uncertainty with rigorous process models. Proceedings AIChE Annual Meeting 2015.
[2] Cozad, A., N. V. Sahinidis and D. C. Miller, Learning surrogate models for simulation-based optimization, AIChE Journal, 2211-2227, 2014.