Search

This paper presents a baseline design and optimization approach developed in Aspen Custom Modeler (ACM) for microtube shell-and-tube exchangers (MSTEs) used for high- and low-temperature recuperation in a 10 MWe indirect supercritical carbon dioxide (sCO 2 ) recompression closed Brayton cycle (RCBC). The MSTE-type recuperators are designed using one-dimensional models with thermal-hydraulic correlations appropriate for sCO 2 and properties models that capture considerable nonlinear changes in CO 2 properties near the critical and pseudo-critical points. Using the successive quadratic programming (SQP) algorithm in ACM, optimal recuperator designs are obtained for either custom or industry-standard microtubes considering constraints based on current advanced manufacturing techniques. The three decision variables are the number of tubes, tube pitch-to-diameter ratio, and tube diameter. Five different objective functions based on different key design measures are considered: minimization of total heat transfer area, heat exchanger volume, metal weight, thermal residence time, and maximization of compactness. Sensitivities studies indicate the constraint on the maximum number of tubes per shell does affect the number of parallel heat exchanger trains but not the tube selection, total number of tubes, tube length and other key design measures in the final optimal design when considering industry-standard tubes. In this study, the optimally designed high- and low-temperature recuperators have 47,000 3/32 in. tubes and 63,000 1/16 in. tubes, respectively. In addition, sensitivities to the design temperature approach and maximum allowable pressure drop are studied, since these specifications significantly impact the optimal design of the recuperators as well as the thermal efficiency and the economic performance of the entire sCO 2 Brayton cycle. [ABSTRACT FROM AUTHOR]

The accurate characterization of the thermodynamic models of a reactive solvent-based CO 2 capture system is essential for adequately capturing system behavior in a process model. Moreover, uncertainty in these models can significantly affect simulation results, although it is often neglected. With this incentive, a model of thermodynamic properties, including vapor-liquid equilibrium (VLE), enthalpy, and solution chemistry, has been developed using a rigorous methodology for the aqueous monoethanolamine (MEA) system as a baseline. The final thermodynamic framework consists of both a deterministic and stochastic model. The deterministic model is developed by regressing parameters of the e-NRTL activity coefficient model in Aspen Plus® to VLE, heat capacity, and heat of absorption data while downselecting the model’s large parameter space through use of information-theoretic criteria. The stochastic model is developed through an uncertainty quantification (UQ) procedure, using the results of the deterministic regression to determine a prior parameter distribution. This prior distribution and the experimental VLE data are used to derive a posterior distribution through Bayesian inference, which is used to represent the final stochastic model. A reaction model in which the kinetics are written in terms of the reaction equilibrium constants, to ensure consistency with the thermodynamics model, is developed. These new thermodynamic and reaction models are incorporated into an existing MEA system process model from the open literature, and the prior and posterior parameter distributions are propagated through the models. This provides valuable insight into the extent to which uncertainty in thermodynamic models affects key process variables, including CO 2 capture efficiency and energy requirement for solvent regeneration. [ABSTRACT FROM AUTHOR]

Optimal selection of energy storage technologies is critical to ensure reliable integration of intermittent and often uncertain renewable energy in electricity grids. The consideration of a diverse set of energy storage technologies is required for a more sustainable deployment of energy storage. We present THESEUS (TecHno-Economic framework for Systematic Energy storage Utilization and downSelection), which is a comprehensive framework for the optimal selection, design and operation of energy storage systems. THESEUS includes rigorous models of major energy storage technologies at different maturity levels, such as thermal storage using phase-change materials or molten salt, cryogenic storage, mechanical storage in the form of compressed air and pumped hydro storage, chemical storage using hydrogen, and electrochemical storage in the form of lithium-ion, sodium sulfur and vanadium flow batteries. An illustrative case study on state-wide prospective energy storage shows that storage integration with fossil power plants could reduce the cost of meeting the grid energy demand by 20%, with mechanical storage as the best suited technology. Although high-temperature thermal storage has low storage efficiency, it is optimal for integration with renewable energy plants. In addition, Li-ion batteries are optimal for high ramping but low storage duration requirements. Such insights can enable the deployment of both existing and emerging energy storage technologies to facilitate a smooth transition to a clean energy future. • Development of a mathematical programming-based decision framework. • Nine energy storage technologies embedded in the optimization framework. • Techno-economic study for various power demand and renewable penetration conditions. • Evaluation of a complex integrated system with storage and power plant dynamics. • Comparison of emerging and mature energy storage technologies for clean energy. [ABSTRACT FROM AUTHOR]

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner's NGCC configuration as representative of existing NGCC units and Svante's rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. The results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process. • Workflow for assessing carbon capture systems in the context of electricity markets • Multiperiod formulation for simultaneous optimization of design and operation • Assess NPV for retrofitting existing NGCC in future electricity market scenarios • Analysis for grid with high percentage of variable renewable energy generation [ABSTRACT FROM AUTHOR]

• Plant-wide process models for non-oxidative methane dehydroaromatization developed. • Conventional and microwave-enhanced thermo-catalytic processes modeled. • A multi-step shale gas to aromatics process via methanol synthesis modeled. • IRR of microwave-enhanced process is the highest among all processes investigated. • NPV of the microwave-enhanced process is superior to other processes. Direct non-oxidative methane dehydroaromatization (DHA) process via conventional and microwave (MW)-assisted thermo-catalytic catalysis is studied. Rate models for methane DHA reactions, including the effect of catalyst deactivation, are developed by using the in-house experimental data. Model results for gas concentration profile and catalyst deactivation are in good agreement with the experimental data. This rate model is then used for the development of dynamic multi-scale, multi-physics commercial-scale reactor models. Total number of fixed bed reactors desired for a cyclic steady state process is estimated. Plant-wide models are then developed for conventional and MW-assisted processes for producing products of desired specifications. Techno-economic analysis of the methane DHA process is undertaken. Economics of these methane DHA processes are compared with the typical multi-step natural gas to aromatics production process via methanol synthesis. Sensitivity of internal rate of return (IRR) and net present value (NPV) to various economic and process parameters such as plant scale, desired rate of return, reactor cost, feedstock and utility cost, catalyst variable cost, and MW reactor cost is studied. Electric equivalent efficiency of the conventional methane DHA process is found to be 69.2 % and 67.3 % at 750 °C and 800 °C, respectively, while the MW-assisted methane DHA process has the electric equivalent efficiency of 48.9 % at 800 °C. IRRs of the conventional methane DHA process at 750 °C and 800 °C, and MW-assisted process are 15.2 %, 17.5 %, and 18.8 %, respectively for a methane feed flowrate of 19,782 kg/h, while the IRR of the multi-step natural gas to aromatics production process is estimated to be 0 % for the same plant scale. Impact of change in the methane price, electricity price, and catalyst cost is found to be considerable on the process economics, while the cost of the MW reactor is found to have negligible impact. [ABSTRACT FROM AUTHOR]

• Modular customizable first-principles model library for coal-fired power plants. • Application of equation-oriented framework for process systems engineering. • Plant-wide large-scale parameter estimation and validation for nonlinear models. • Energy system diagnosis and optimization through advanced process modeling. As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. To demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation. [ABSTRACT FROM AUTHOR]

Activated carbons (AC) from lignocellulosic biomass feedstocks are used in a broad range of applications, especially for electrochemical devices such as supercapacitor electrodes. Limited studies of environmental and economic impacts for AC supercapacitor production have been conducted. Thus, this paper evaluated the environmental and economic impacts of AC produced from lignocellulosic biomass for energy-storage purposes. The life cycle assessment (LCA) was employed to quantify the potential environmental impacts associated with AC production via the proposed processes including feedstock establishment, harvest, transport, storage, and in-plant production. A techno-economic model was constructed to analyze the economic feasibility of AC production, which included the processes in the proposed technology, as well as the required facility installation and management. A base case, together with two alternative scenarios of KOH-reuse and steam processes for carbon activation, were evaluated for both environmental and economic impacts, while the uncertainty of the net present value (NPV) of the AC production was examined with seven economic indicators. Our results indicated that overall "in-plant production" process presented the highest environmental impacts. Normalized results of the life-cycle impact assessment showed that the AC production had environmental impacts mainly on the carcinogenics, ecotoxicity, and non-carcinogenics categories. We then further focused on life cycle analysis from raw biomass delivery to plant gate, the results showed that "feedstock establishment" had the most significant environmental impact, ranging from 50.3% to 85.2%. For an activated carbon plant producing 3000 kg AC per day in the base case, the capital cost would be USD 6.66 million, and annual operation cost was found to be USD 15.46 million. The required selling price (RSP) of AC was USD 16.79 per kg, with the discounted payback period (DPB) of 9.98 years. Alternative cases of KOH-reuse and steam processes had GHG emissions of 15.4 kg CO2 eq and 10.2 kg CO2 eq for every 1 kg of activated carbon, respectively. Monte Carlo simulation showed 49.96% of the probability for an investment to be profitable in activated carbon production from lignocellulosic biomass for supercapacitor electrodes. [ABSTRACT FROM AUTHOR]

• Microwave assisted direct methane conversion to C2 and aromatics. • Enhanced methane conversion under microwave irradiation. • Microwave induces the formation of hot metal sites. Microwave-assisted catalysis offers great promise as an "intensified" technology for chemical processing. The present study investigates microwave-assisted direct conversion of methane with focus on the design and evaluation of microwave-sensitive H-(Fe)ZSM-5 catalysts for the existence of hotspot formation. Isomorphous substituted H-(Fe)ZSM-5 catalysts are tuned to identify key design parameters that control their microwave sensitivity. Increasing the amount of Al and Fe substitution in the zeolite lattice is found to result in higher microwave sensitivity due to improved dielectric properties and hence facilitated microwave heating of the catalyst bed. Comparison between the performance of these catalysts in a conventional thermally-heated (CH) fixed-bed and a microwave (MW) reactor allows identification of the effect of microwave irradiation on the catalyst activity. Methane conversion is drastically enhanced at MW conditions (from 3% at CH conditions to 40% at MW conditions), indicating accelerated methane activation over the metal site and hence suggesting a hot metal site despite much lower catalyst bulk temperature measured in the microwave reactor. The existence of metal hotspot formation is further supported by the reaction product distribution and spent catalyst analysis. At MW conditions, C 2 (ethane and ethylene) and coke (mainly carbon nanotubes and nanofibers) selectivity are much higher at the cost of aromatics make compared to CH conditions. This can be explained by reduced aromatization activity at the Brønsted acid sites of the zeolite due to its lower relative temperature of the zeolite. The much-enhanced metal aggregation observed in the spent catalyst from the MW reactor along with the distribution of coke species further confirms the existence of selective heating (i.e. hotspot formation) occurring at the active metal site in the catalyst at MW conditions. [ABSTRACT FROM AUTHOR]

• Developed a methodology for sequential Bayesian design of experiments. • Minimized the maximum model prediction uncertainty for key output variables. • Methodology applied to an aqueous monoethanolamine pilot plant. • Two iteration resulted in 50% reduction in uncertainty of CO 2 capture prediction. • Methodology is generic and can be readily applied to other process systems. In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis. [ABSTRACT FROM AUTHOR]

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Modern technologies transform biomass into commodity chemicals, biofuels, and solid charcoal, making it appear as a renewable resource rather than organic waste. The effectiveness of Mo, Fe, Co, and Ni metal catalysts was investigated during the gasification of lignocellulosic pinewood. The primary goal was to compare the performance of iron and nickel catalysts in the low- and high-pressure production of syngas from pinewood. This is the first study that has reported high-pressure gasification of pinewood without the use of an external gasifying agent, producing syngas containing hydrogen, carbon monoxide, and carbon dioxide along with considerable amounts of methane with or without a catalyst. Also, the same gasification at low pressures was compared. In this study, the iron catalyst produces syngas more efficiently at higher pressure and 800 °C, and contains 43 mol % H 2 , 22 mol % CO 2 , 26 mol % CH 4 , and 8 mol % CO in comparison to the nickel catalyst. High pressure produces a large amount of methane too. The nickel catalyst produces higher syngas at low pressure and 850 °C, and contains 55 mol % H 2 , 9 mol % CO 2 , 5 mol % CH 4 , and 30 mol % CO. Low-pressure gasification produces less amounts of CH 4 and CO 2 . Also, the H 2 /CO ratio is ∼1.81 using the nickel catalyst at low pressures, which is good for utilizing syngas as a feedstock. These results highlight the importance of catalyst selection, reactor configuration, and operating circumstances in adjusting gasification product composition. The study's findings provide information about optimizing syngas production from pinewood, which is critical for the development of sustainable and efficient energy conversion technologies., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

A novel reactor methodology was developed for chemical looping ammonia synthesis processes using microwave plasma for pre-activation of the stable dinitrogen molecule before reaching the catalyst surface. Microwave plasma-enhanced reactions benefit from higher production of activated species, modularity, quick startup, and lower voltage input than competing plasma-catalysis technologies. Simple, economical, and environmentally benign metallic iron catalysts were used in a cyclical atmospheric pressure synthesis of ammonia. Rates of up to 420.9 μmol min -1 g -1 were observed under mild nitriding conditions. Reaction studies showed that both surface-mediated and bulk-mediated reaction domains were found to exist depending on the time under plasma treatment. The associated density functional theory (DFT) calculations indicated that a higher temperature promoted more nitrogen species in the bulk of iron catalysts but the equilibrium limited the nitrogen converion to ammonia, and vice versa. Generation of vibrationally active N 2 and, N 2 + ions is associated with lower bulk nitridation temperatures and increased nitrogen contents versus thermal-only systems. Additionally, the kinetics of other transition metal chemical looping ammonia synthesis catalysts (Mn and CoMo) were evaluated by high-resolution time-on-stream kinetic analysis and optical plasma characterization. This study sheds new light on phenomena arising in transient nitrogen storage, kinetics, effect of plasma treatment, apparent activation energies, and rate-limiting reaction steps.