Control Structure Selection for the Elevated-Pressure Air Separation Unit in an IGCC Power Plant: Self-Optimizing Control Structure for Economical Operation

The air separation unit (ASU) is one of the core elements of integrated gasification combined cycle (IGCC) power plants. The ASU separates air into pure oxygen and nitrogen, to be sent to the gasifier and the gas turbine, respectively. This system consumes about 10% of the gross power output generated in IGCC, so its economical operation is important for lowering the overall power generation cost. The use of an elevated-pressure air separation unit (EP ASU), in which the operating pressure is higher than in a conventional ASU, is known to lead to significant energy savings. In this research, controlled variable selection for an EP ASU was studied, considering both the controllability and economics, that is, with the objective of maintaining economically near-optimal operations in the presence of anticipated load changes. The main tool used for this was the so-called “minimum singular value rule” within the overall framework of self-optimizing control (SOC). For the purpose of selecting and testing self-optimizing control structures, equation-based modeling of EP ASU was carried out and implemented on the commercial software platform gPROMS. Then, the minimum singular value rule was applied using steady-state gain matrices (obtained from the simulator) to select a small number of candidate sets for controlled variables, to which rigorous analyses based on nonlinear simulation and optimization could be applied to pick the top choice. Before the minimum singular value rule was applied, however, certain process insights and heuristics were used to reduce the number of candidate sets down to a manageable level. The economic losses as a result of adopting a fixed control structure were assessed by comparing the hourly operating costs achieved under SOC with the equivalent values obtained by performing full nonlinear optimizations for the given scenarios. In addition, for the suggested control structure, proportional plus integral (PI) control loops were designed, and their dynamic performance was tested in order to make sure that it is attractive in terms of not only economics but also controllability. The finally selected control structure is compared with those presented in previous works

Flexible operation of modular electrochemical CO2 reduction processes

Electrochemical CO2 reduction (eCO2R) is an emerging technology that is capable of producing various organic chemicals from CO2, but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO2-electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO2R process. We formulate the economically optimal operation of an eCO2R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO2R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO2R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving

Flexible operation of modular electrochemical CO2 reduction processes

Electrochemical CO2 reduction (eCO2R) is an emerging technology that is capable of producing various organic chemicals from CO2, but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO2-electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO2R process. We formulate the economically optimal operation of an eCO2R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO2R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO2R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving

Impacts of deploying co-electrolysis of CO2 and H2O in the power generation sector: A case study for South Korea

This work analyzes the impacts of deploying a Power-to-Gas technology in the power generation sector in South Korea by 2050. The Power-to-Gas technology of interest is the low-temperature co-electrolysis of CO2 and H2O, which is an emerging technology for electrochemically converting them to syngas. Particularly, excess electricity available from intermittent renewable energy resources is intended to be the main energy source for the co-electrolysis. A conceptual design of the co-electrolysis process is carried out to calculate its performance data including mass balances, energy demand, and capital investment. Based on them, a temporal energy system model is developed using the TIMES (The Integrated MARKAL-EFOM System) model generator. The conclusion is that deploying the co-electrolysis process in the Korean power generation sector can reduce greenhouse gas emissions and also save the overall system cost when the syngas production cost is lower than the purchasing cost of liquid natural gas. The beneficial impacts are limited by the amount of available excess electricity and the co-firing ratio limit in the gas-fired power plants. Finally, the overpotential and current density, as uncertain parameters of the co-electrolysis process, are found to affect the syngas production cost most strongly