Modeling and Optimization of a Novel Chilled Ammonia Absorption Process and Amine-Appended Metal-Organic Frameworks for CO2 Capture

Post-combustion capture is one of the leading technologies for CO2 abatement from anthropogenic sources which have contributed significantly to the rise of atmospheric greenhouse gases. Specifically, solvent-based capture post-combustion processes are the industry standard but can suffer drawbacks such as high energy penalties and corrosion. In this work, two possible improvements are investigated which have been recently proposed in the literature. The first is aqueous ammonia as a capture solvent which has been shown to have several advantages including, but not limited to, a lower regeneration energy. The second is a novel solid sorbent, an amine-appended metal-organic framework (MOF). The MOF exhibits several promising attributes, namely, a step-shaped adsorption isotherm which leads to lower working capacities and lower regeneration energies when compared to traditional solid sorbents. The overall goal of this work is to develop rigorous mathematical models which can be used for process design and economic evaluation of these technologies. First, an integrated mass transfer model is developed for the chilled ammonia process (CAP). This model is developed using a simultaneous regression approach that has been recently proposed in the literature with parameter estimation performed using data from a pilot plant source and wetted-wall column. The optimally estimated parameters are shown to have a lower prediction error to validation data than parameters found in literature. The integrated mass transfer model is then used to develop a model for a novel chilled ammonia process. The process includes a NH3 abatement system which utilizes a reverse osmosis membrane to aid in separation and reduce the energy penalty. Simulation of the process shows that the membrane can significantly reduce the energy requirement of the reboiler, condenser, and cooler in the abatement section. Uncertainty of the estimated mass transfer parameters is quantified using a fully Bayesian approach which is demonstrated to show a significant reduction in the prediction uncertainty of key process indicators. Second, isotherm and kinetic models are developed for amine-appended MOFs, dmpn-Mg2(dobpdc) and Mg2(dobpdc)(3-4-3). The step-shaped adsorption isotherms exhibited by these MOFs present a modeling challenge since many of the traditional isotherm models are unable to capture step transitions. Three isotherm models are examined in this work, a weighted dual-site Langmuir model found in literature, a dual-site Sips model developed in this work, and an extended weighted Langmuir model also developed in this work. Parameter estimation is performed using available isotherm data and it is shown that the models are able to predict the CO2 adsorption data well. A kinetic model is then developed using a linear driving force for mass transfer which does an excellent job at predicting time dependent TGA data. An additional goal of this work is development of a chemistry-based model for functionalized solid sorbents that aims to capture the underlying adsorption reaction mechanisms which are not typically considered in solid sorbent modeling. As part of this model, optimal reaction set selection is performed since the reaction pathways for dmpn-Mg2(dobpdc) are still relatively unknown. Parameter estimation is performed, and it is found that the chemistry-based model significantly outperforms the Sips isotherm model with regards to prediction error and other model building criteria. To aid in the evaluation of the commercial feasibility of the MOF, equation-oriented mathematical models for a fixed bed contactor and moving bed contactor are developed. The contactors are then to simulate industrial scale CO2 capture process for coal based and NGCC based flue gas. Using developed cost models, techno-economic analysis and optimization of these processes is then performed and it is found that efficient thermal management can make these MOFs viable alternatives for CO2 capture processes

Estimation of kinetic parameters in a chromatographic separation model via Bayesian inference

A modelagem de processos de adsorção tem sido empregada com frequência nas indústrias químicas, petroquímicas e refinarias, por exemplo para separação e purificação de misturas em unidade de Leito Móvel Simulado (LMS). Na representação matemática do modelo, a determinação de parâmetros é um passo importante para o projeto de condições cromatográficas para a separação contínua, em processos do tipo LMS. Este trabalho tem por objetivo a análise de estimativa de parâmetros em processos de adsorção, usando um sistema cromatográfico com uma coluna, para a separação das substâncias Glicose e Frutose. Investiga-se o uso da abordagem Bayesiana, através de métodos de Monte Carlo via Cadeias de Markov (MCMC), assim como o uso da abordagem da máxima verossimilhança, utilizando duas técnicas estocásticas diferentes, o Algoritmo de Colisão de Partículas (PCA - Particle Collision Algorithm), e o Algoritmo de Otimização por Enxame de Partículas (PSO - Particle Swarm Optimization) para executar a tarefa de minimização da função objetivo. Diferentes casos são apresentados com o objetivo de analisar a significância estatística das estimativas obtidas para os parâmetros, fazendo-se uma comparação crítica entre a solução via inferência Bayesiana e via minimização da função objetivo com métodos estocásticos. Os resultados obtidos demonstram que o uso da abordagem Bayesiana fornece uma proposta vantajosa para a estimativa de parâmetros em transferência de massa, oferecendo resultados com maior riqueza de informação estatística.The modeling of adsorption processes appears quite frequently in the chemical industry, petrochemical plants and refineries, for example for separation and purification of mixtures in Simulated Moving Bed (SMB) units. In the mathematical formulation, the accurate determination of the model parameters is an important step for the design of chromatographic conditions for continuous separation in SMB processes. This work is aimed at the estimation of the model parameters in adsorption processes, using a chromatographic column for the separation of glucose and fructose. The Bayesian framework for inverse problems is investigated through the implementation of Markov Chain Monte Carlo methods (MCMC) and a critical comparison against the classical Maximum Likelihood approach, with the minimization of the objective function via two different stochastic techniques, namely the Particle Collision Algorithm (PCA), and the Particle Swarm Optimization (PSO) is performed. Different cases are presented in order to investigate the statistical significance of the estimates obtained, and perform comparisons between the solution via Bayesian inference and via the minimization of the objective function with the stochastic methods. The results demonstrate that the Bayesian approach employs less computational effort to achieve estimates with comparable statistical information.Peer Reviewe

Non-linear system Identification and control of Solvent-Based Post-Combustion CO2 Capture Process

Solvent-based post-combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control system design. Typically, first principles models are used, however, they usually require comprehensive knowledge and in-depth understanding of the process. In addition, the high computational time required and high complexity of the first principles models makes it unsuitable for control system design implementation. This thesis is aimed at the development of a reliable dynamic model via system identification technique as well as a suitable process control strategy for the solvent-based post-combustion CO2 capture process. The nonlinear autoregressive with exogenous (NARX) inputs model is employed to represent the relationship between the input variables and output variables as two multiple-input single-output (MISO) sub-systems. The forward regression with orthogonal least squares (FROLS) algorithm is implemented to select an accurate model structure that best describes the dynamics within the process. The prediction performance of the identified NARX models is promising and shows that the models capture the underlying dynamics of the CO2 capture process. The model obtained was adopted for various process control system design of the solvent-based PCC process (conventional PI, MPC, and NMPC). For the conventional PI controller design, multivariable control analysis was carried out to determine a suitable control structure. Control performance evaluation of the control schemes reveals that the NMPC scheme was suitable to control the solvent-based PCC process at flexible operations. Findings obtained from the thesis underlines the advancement in dynamic modelling and control implementation of solvent-based PCC process

New approaches for the real-time optimization of process systems under uncertainty

In the process industry, the economical operation of systems is of utmost importance for stakeholders to remain competitive. Moreover, economic incentives can be used to drive the development of sustainable processes, which must be deployed to ensure continued human and ecological welfare. In the process systems engineering paradigm, model predictive control (MPC) and real-time optimization (RTO) are methods used to achieve operational optimality; however, both methods are subject to uncertainty, which can adversely affect their performance. Along with the challenges of uncertainty, formulations of economic optimization problems are largely problem-specific as process utilities and products vary significantly by application; thus, many nascent processes have not received a tailored economic optimization treatment. In this thesis, the focus is on avenues of economic optimization under uncertainty, namely, the two-step RTO method, which updates process models via parameters; and the modifier adaptation (MA) method, which updates process models via error and gradient correction. In the case of parametric model uncertainty, the two-step RTO method is used. The parameter estimation (PE) step that accompanies RTO requires plant measurements that are often noisy, which can cause the propagation of noise to the parameter estimates and result in poor RTO performance. In the present work, a noise-abatement scheme is proposed such that high-fidelity parameter estimates are used to update a process model for economic optimization. This is achieved through parameter estimate bootstrapping to compute bounds and determine the measurement-set that results in the lowest parameter variation; thus, the scheme is dubbed low-variance parameter estimation (lv-PE). This method is shown to result in improved process economics through truer set points and reduced dynamic behaviour. In the case of structural model mismatch (i.e., unmodelled phenomena), the MA approach is used, whereby gradient modifier (i.e., correction) terms must be recursively estimated until convergence. These modifier terms require plant perturbations to be performed, which incite time-consuming plant dynamics that delay operating point updates. In cases with frequent disturbances, MA may have poor performance well as there is limited time to refine the modifiers. Herein, a partial modifier adaptation (pMA) method is proposed, which selects a subset of modifications to be made, thus reducing the number of necessary perturbations. Through this reduced experimental burden, the operating point refinement process is accelerated resulting in quicker convergence to advantageous operating points. Additionally, constraint satisfaction during this refinement process can also result in poor performance via wasted below-specification products. Accordingly, the pMA method also includes an adjustment step that can drive the system to constraint-satisfying regions at each iteration. The pMA method is shown to economically outperform both the standard MA method as well as a related directional MA method in cases with frequent periodic disturbances. The economic optimization methods described above are implemented in novel processes to improve their economics, which can incite further technological uptake. Post-combustion carbon capture (PCC) is the most advanced carbon capture technology as it has been investigated extensively. PCC takes industrial flue gases and separates the carbon dioxide for later repurposing or storage. Most PCC operating schemes make decisions using simplified models since a mechanistic PCC model is large and difficult to solve. To this end, this thesis provides the first robust MPC that can address uncertainty in PCC with a mechanistic model. The advantage of the mechanistic model in robust optimal control is that it allows for a precise treatment of uncertainties in phenomenological parameters. Using the multi-scenario approach, discrete realizations of the uncertain parameters inside a given uncertainty region can be incorporated into the controller to produce control actions that result in a robust operation in closed-loop. In the case of jointly uncertainty activity coefficients and flue gas flowrates, the proposed robust MPC is shown to lead to improved performance with respect to a nominal controller (i.e., one that does not hedge against uncertainty) under various operational scenarios. In addition to the PCC robust control problem, the mechanistic model is used for economic optimization and state estimation via RTO and moving horizon estimation (MHE) layers respectively. While the former computes economical set points, the latter uses few measurements to compute the full system state, which is necessary for the controller that uses a mechanistic model. These layers are integrated to operate the system economically via a new economic function that accounts for the most significant economic aspects of PCC, including the carbon economy, energy, chemical, and utility costs. A new proposed MPC layer is novel in its ability to enable flexible control of the plant by manipulating fresh material streams to impact CO2 capture and the MHE layer is the first to provide accurate system estimates to the controller with realistically accessible measurements. A joint MPC-MHE-RTO scheme is deployed for PCC, which is shown to lead to more economical steady-state operation compared to constant set point counterfactuals under cofiring, diurnal operation, and price variation scenarios. The lv-PE scheme is also deployed for the PCC system where it is found to improve set point economics with respect to traditional PE methods. The improvements are observed to occur through reduced emissions and more efficient energy used, thus having environmental co-benefits. Moreover, the lv-PE algorithm is used for uncertainty quantification to develop a robust RTO that leads to more conservative set points (i.e., less economic improvement) but lower set point variation (i.e., less control burden). The methodologies developed in this PhD thesis provide improvements in efficacy as well as applicability of online economic optimization in engineering applications, where uncertainty is often present. These can be deployed by both academic as well as industrial practitioners that wish to improve the economic performance on their processes

61st Annual Rocky Mountain Conference on Magnetic Resonance

Final program, abstracts, and information about the 61st annual meeting of the Rocky Mountain Conference on Magnetic Resonance, co-endorsed by the Colorado Section of the American Chemical Society and the Society for Applied Spectroscopy. Held in Copper Mountain, Colorado, July 25-29, 2022

Efficient targeted optimisation for the design of pressure swing adsorption systems for CO2 capture in power plants

Pressure swing adsorption (PSA) is a cyclic adsorption process for gas separation and purification, and can be used in a variety of industrial applications, for example, hydrogen purification and dehydration. PSA is, due to its low operational cost and its ability to efficiently separate CO2 from flue gas, a promising candidate for post-combustion carbon capture in power plants, which is an important link in the Carbon Capture and Storage technology chain. PSA offers many design possibilities, but to optimise the performance of a PSA system over a wide range of design choices, by experimental means, is typically too costly, in terms of time and resources required. To address this challenge, computer experiments are used to emulate the real system and to predict the performance. The system of PDAEs that describes the PSA process behaviour is however typically computationally expensive to simulate, especially as the cyclic steady state condition has to be met. Over the past decade, significant progress has been made in computational strategies for PSA design, but more efficient optimisation procedures are needed. One popular class of optimisation methods are the Evolutionary algorithms (EAs). EAs are however less efficient for computationally expensive models. The use of surrogate models in optimisation is an exciting research direction that allows the strengths of EAs to be used for expensive models. A surrogate based optimisation (SBO) procedure is here developed for the design of PSA systems. The procedure is applicable for constrained and multi-objective optimisation. This SBO procedure relies on Kriging, a popular surrogate model, and is used with EAs. The main application of this work is the design of PSA systems for CO2 capture. A 2- bed/6-step PSA system for CO2 separation is used as an example. The cycle configuration used is sufficiently complex to provide a challenging, multi-criteria example

Investigation of Volatile Organic Compounds (VOCs) released as a result of spoilage in whole broccoli, carrots, onions and potatoes with HS-SPME and GC-MS

Vegetable spoilage renders a product undesirable due to changes in sensory characteristics. The aim of this study was to investigate the change in the fingerprint of VOC composition that occur as a result of spoilage in broccoli, carrots, onions and potatoes. SPME and GC-MS techniques were used to identify and determine the relative abundance of VOC associated with both fresh and spoilt vegetables. Although a number of similar compounds were detected in varying quantities in the headspace of fresh and spoilt samples, certain compounds which were detected in the headspace of spoilt vegetables were however absent in fresh samples. Analysis of the headspace of fresh vegetables indicated the presence of a variety of alkanes, alkenes and terpenes. Among VOCs identified in the spoilt samples were dimethyl disulphide and dimethyl sulphide in broccoli; Ethyl propanoate and Butyl acetate in carrots; 1-Propanethioland 2-Hexyl-5-methyl-3(2H)-furanone in onions; and 2, 3-Butanediol in potatoes. The overall results of this study indicate the presence of VOCs that can serve as potential biomarkers for early detection of quality deterioration and in turn enhance operational and quality control decisions in the vegetable industry

Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC