Stephen E. Zitney

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Optimization of IGCC processes with reduced order CFD models

Abstract Integrated gasification combined cycle (IGCC) plants have significant advantages for efficient power generation with carbon capture. Moreover, with the development of accurate CFD models for gasification and combined cycle combustion, key units of these processes can now be modeled more accurately. However, the integration of CFD models within steady-state process simulators, and subsequent optimization of the integrated system, still presents significant challenges. This study describes the development and demonstration of a reduced order modeling (ROM) framework for these tasks. The approach builds on the concepts of co-simulation and ROM development for process units described in earlier studies. Here we show how the ROMs derived from both gasification and combustion units can be integrated within an equation-oriented simulation environment for the overall optimization of an IGCC process. In addition to a systematic approach to ROM development, the approach includes validation tasks for the CFD model as well as closed-loop tests for the integrated flowsheet. This approach allows the application of equation-based nonlinear programming algorithms and leads to fast optimization of CFD-based process flowsheets. The approach is illustrated on two flowsheets based on IGCC technology.

Process/equipment co-simulation for design and analysis of advanced energy systems

Abstract The grand challenge facing the power and energy industries is the development of efficient, environmentally friendly, and affordable technologies for next-generation energy systems. To provide solutions for energy and the environment, the U.S. Department of Energys (DOE) National Energy Technology Laboratory (NETL) and its research partners in industry and academia are relying increasingly on the use of sophisticated computer-aided process design and optimization tools. In this paper, we describe recent progress toward developing an Advanced Process Engineering Co-Simulator (APECS) for the high-fidelity design, analysis, and optimization of energy plants. The APECS software system combines steady-state process simulation with multiphysics-based equipment simulations, such as those based on computational fluid dynamics (CFD). These co-simulation capabilities enable design engineers to optimize overall process performance with respect to complex thermal and fluid flow phenomena arising in key plant equipment items, such as combustors, gasifiers, turbines, and carbon capture devices. In this paper we review several applications of the APECS co-simulation technology to advanced energy systems, including coal-fired energy plants with carbon capture. This paper also discusses ongoing co-simulation R&D activities and challenges in areas such as CFD-based reduced-order modeling, knowledge management, advanced analysis and optimization, and virtual plant co-simulation. Continued progress in co-simulation technology – through improved integration, solution, and deployment – will have profound positive impacts on the design and optimization of high-efficiency, near-zero emission fossil energy systems.

Plant-wide control system design: Primary controlled variable selection

Abstract This work is focused on the development of a rigorous, model-based approach for the selection of primary controlled variables as part of a plant-wide control system design methodology. Controlled variables should be selected for their self-optimizing control performance and controllability while ensuring satisfactory performance in terms of dead-time and closed loop interactions. This work has considered both self-optimizing and control performance as well as has addressed issues related to loop-interactions and superstructure constraints. The new three-stage approach developed in this work results in a large-scale, constrained, mixed-integer multi-objective optimization problem. For solving this problem, a parallelized, bi-directional branch and bound algorithm with dynamic search strategies has been developed to solve the problem on large computer clusters. The proposed approach is then applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with CO 2 capture.

Minimization of Water Consumption under Uncertainty for a Pulverized Coal Power Plant

Coal-fired power plants are large water consumers. Water consumption in thermoelectric generation is strongly associated with evaporation losses and makeup streams on cooling and contaminant removal systems. Thus, minimization of water consumption requires optimal operating conditions and parameters, while fulfilling the environmental constraints. Several uncertainties affect the operation of the plants, and this work studies those associated with weather. Air conditions (temperature and humidity) were included as uncertain factors for pulverized coal (PC) power plants. Optimization under uncertainty for these large-scale complex processes with black-box models cannot be solved with conventional stochastic programming algorithms because of the large computational expense. Employment of the novel better optimization of nonlinear uncertain systems (BONUS) algorithm, dramatically decreased the computational requirements of the stochastic optimization. Operating conditions including reactor temperatures and pressures; reactant ratios and conditions; and steam flow rates and conditions were calculated to obtain the minimum water consumption under the above-mentioned uncertainties. Reductions of up to 6.3% in water consumption were obtained for the fall season when process variables were set to optimal values. Additionally, the proposed methodology allowed the analysis of other performance parameters like gas emissions and cycle efficiency which were also improved.

Coupled CFD and Process Simulation of a Fuel Cell Auxiliary Power Unit

A high-temperature auxiliary power unit (APU) based on solid oxide fuel cell (SOFC) technology is analyzed in this study using coupled computational fluid dynamics (CFD) and process simulation. The tightly integrated process flowsheet consists of a reformer, desulfurizer, SOFC stack, combustor, and various heat exchange and rotating equipment items. A detailed three-dimensional CFD model is used to represent the cross-flow, planar SOFC. Process simulations are used to calculate the overall material and energy balances. Coupled CFD and process simulations are performed over a range of fuel cell currents to generate a voltage current curve and analyze the effect of current on fuel utilization, power density, and overall system efficiency. The fuel cell APU system considered here generated 4.3 kW of power and yielded a maximum fuel-to-electricity conversion efficiency of 45.4% at a current of 18 amperes. Integrated CFD and process simulations provide a better understanding of the fluid mechanics that drive overall performance and efficiency of fuel cell systems. In addition, the analysis of the fuel cell using CFD is not done in isolation but within the context of the whole APU process.Copyright

Rigorous-simulation pinch-technology refined approach for process synthesis of the water-gas shift reaction system in an IGCC process with carbon capture

Integrated gasification combined cycle (IGCC) technology is becoming increasingly more competitive among advanced power generation systems suitable for carbon capture. As an emerging technology, many different IGCC process configurations have been heuristically proposed to meet even more aggressive economic and environmental goals. One attractive design combines gasification with a water–gas shift (WGS) reaction system, pressure swing adsorption, and chemical-looping combustion (CLC) for CO2 removal prior to feeding the fuel gas to the combined cycle for power production. The WGS reaction step is required to convert CO to CO2 and the extent of conversion is determined by the degree of carbon capture required in the CLC step. As a first towards optimizing the overall energy efficiency of this IGCC process, we apply heat exchanger network synthesis (HENS) to the WGS reaction system. This particular part of the process was chosen because of its evident integration potential (steam required for the WGS reactions can be generated by recovering energy released by the same reactions) and the influence of some of the gasifier parameters (temperature and pressure) on its performance and on all the subsequent parts of the process. After generating alternative designs using Aspen Energy Analyzer (AEA), the HENS problem was formulated in the sequential-modular Aspen Plus simulator using a process superstructure approach and solved by mixed integer nonlinear programming (MINLP) algorithms. The HENS capability is implemented as CAPE-OPEN (CO) compliant unit operation and makes use of MINLP algorithms, namely Generalized Benders Decomposition (GBD), Outer Approximation (OA), Equality Relaxation (ER), Augmented Penalty (AP), and Simulated Annealing (SA). This MINLP-based HENS was used in the CO-compliant Aspen Plus simulator to obtain a design for the WGS reaction system that provided a cost of energy for the IGCC system with CO2 capture that was 28% lower than the base case.

Evaluation of methods for thermal management in a coal-based SOFC turbine hybrid through numerical simulation

Managing the temperatures and heat transfer in the fuel cell of a solid oxide fuel cell (SOFC) gas turbine (GT) hybrid fired on coal syngas presents certain challenges over a natural gas based system, in that the latter can take advantage of internal reforming to offset heat generated in the fuel cell. Three coal based SOFC/GT configuration designs for thermal management in the main power block are evaluated using steady state numerical simulations developed in ASPEN Plus. A comparison is made on the basis of efficiency, operability issues and component integration. To focus on the effects of different power block configurations, the analysis assumes a consistent syngas composition in each case, and does not explicitly include gasification or syngas cleanup. A fuel cell module rated at 240MW was used as a common basis for three different methods. Advantages and difficulties for each configuration are identified in the simulations.

Stochastic modeling and multi-objective optimization for the APECS system

Abstract The Advanced Process Engineering Co-Simulator (APECS), developed at the U.S. Department of Energys (DOE) National Energy Technology Laboratory, is an integrated software suite that enables the process and energy industries to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. The APECS system uses the process-industry standard CAPE-OPEN (CO) interfaces to combine equipment models and commercial process simulation software with powerful analysis and virtual engineering tools. The focus of this paper is the CO-compliant stochastic modeling and multi-objective optimization capabilities provided in the APECS system for process optimization under uncertainty and multiple and sometimes conflicting objectives. The usefulness of these advanced analysis capabilities is illustrated using a simulation and multi-objective optimization of an advanced coal-fired, gasification-based, zero-emissions electricity and hydrogen generation facility with carbon capture.

Dynamic Maximization of Oxygen Yield in an Elevated-Pressure Air Separation Unit Using Multiple Model Predictive Control

In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain optimal flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.