Dynamic Optimization Algorithms for Baseload Power Plant Cycling under Variable Renewable Energy

The growing deployment of variable renewable energy (VRE) sources, such as wind and solar, is mainly due to the decline in the cost of renewable technologies and the increase of societal and cultural pressures. Solar and wind power generation are also known to have zero marginal costs and fuel emissions during dispatch. Thereby, the VRE from these sources should be prioritized when available. However, the rapid deployment of VRE has heightened concerns regarding the challenges in the integration between fossil-fueled and renewable energy systems. The high variability introduced by the VRE as well as the limited alignment between demand and wind/solar power generation led to the increased need of dispatchable energy sources such as baseload natural gas- and coal-fired power plants to cycle their power outputs more often to reliably supply the net load. The increasing power plant cycling can introduce unexpected inefficiencies into the system that potentially incur higher costs, emissions, and wear-and-tear, as the power plants are no longer operating at their optimal design points. In this dissertation, dynamic optimization algorithms are developed and implemented for baseload power plant cycling under VRE penetration. Specifically, two different dynamic optimization strategies are developed for the minute and hourly time scales of grid operation. The minute-level strategy is based on a mixed-integer linear programming (MILP) formulation for dynamic dispatch of energy systems, such as natural gas- and coal-fired power plants and sodium sulfur batteries, under VRE while considering power plant equipment health-related constraints. The hourly-level strategy is based on a Nonlinear Multi-objective dynamic real-time Predictive Optimization (NMPO) implemented in a supercritical pulverized coal-fired (SCPC) power plant with a postcombustion carbon capture system (CCS), considering economic and environmental objectives. Different strategies are employed and explored to improve computational tractability, such as mathematical reformulations, automatic differentiation (AD), and parallelization of a metaheuristic particle swarm optimization (PSO) component. The MILP-based dynamic dispatch framework is used to simulate case studies considering different loads and renewable penetration levels for a suite of energy systems. The results show that grid flexibility is mostly provided by the natural gas power plant, while the batteries are used sparingly. Additionally, considering the post-optimization equivalent carbon analysis, the environmental performance is intrinsically connected to grid flexibility and the level of VRE penetration. The stress results reinforce the necessity of further considering and including equipment health-related constraints during dispatch. The results of the NMPO successfully implemented for a large-scale SCPC-CCS show that the optimal compromise is automatically chosen from the Pareto front according to a set of weights for the objectives with minimal interaction between the framework and the decision maker. They also indicate that to setup the optimization thresholds and constraints, knowledge of the power system operations is essential. Finally, the market and carbon policies have an impact on the optimal compromise between the economic and environmental objectives

State Estimation, Covariance Estimation, and Economic Optimization of Semi-Batch Bioprocesses

One of the most critical aspects of any chemical process engineer is the ability to gather, analyze, and trust incoming process data as it is often required in control and process monitoring applications. In real processes, online data can be unreliable due to factors such as poor tuning, calibration drift, or mechanical drift. Outside of these sources of noise, it may not be economically viable to directly measure all process states of interest (e.g., component concentrations). While process models can help validate incoming process data, models are often subject to plant-model mismatches, unmodeled disturbances, or lack enough detail to track all process states (e.g., dissolved oxygen in a bioprocess). As a result, directly utilizing the process data or the process model exclusively in these applications is often not possible or simply results in suboptimal performance. To address these challenges and achieve a higher level of confidence in the process states, estimation theory is used to blend online measurements and process models together to derive a series of state estimates. By utilizing both sources, it is possible to filter out the noise and derive a state estimate close to the true process conditions. This work deviates from the traditional state estimation field that mostly addresses continuous processes and examines how techniques such as extended Kalman Filter (EKF) and moving horizon estimation (MHE) can be applied to semi-batch processes. Additionally, this work considers how plant-model mismatches can be overcome through parameter-based estimation algorithms such as Dual EKF and a novel parameter-MHE (P-MHE) algorithm. A galacto-oligosaccharide (GOS) process is selected as the motivating example as some process states are unable to be independently measured online and require state estimation to be implemented. Moreover, this process is representative of the broader bioprocess field as it is subject to high amounts of noise, less rigorous models, and is traditionally operated using batch/semi-batch reactors. In conjunction with employing estimation approaches, this work also explores how to effectively tune these algorithms. The estimation algorithms selected in this work require careful tuning of the model and measurement covariance matrices to balance the uncertainties between the process models and the incoming measurements. Traditionally, this is done via ad-hoc manual tuning from process control engineers. This work modifies and employs techniques such as direct optimization (DO) and autocovariance least-squares (ALS) to accurately estimate the covariance values. Poor approximation of the covariances often results in poor estimation of the states or drives the estimation algorithm to failure. Finally, this work develops a semi-batch specific dynamic real-time optimization (DRTO) algorithm and poses a novel costing methodology for this specific type of problem. As part of this costing methodology, an enzyme specific cost scaling correlation is proposed to provide a realistic approximation of these costs in industrial contexts. This semi-batch DRTO is combined with the GOS process to provide an economic analysis using Kluyveromyces lactis (K. lactis) β-galactosidase enzyme. An extensive literature review is carried out to support the conclusions of the economic analysis and motivate application to other bioprocesses

KPI oriented approach for real-time optimization

Indicadores-chave de desempenho (Key-performance Indicators - KPIs) são ferramentas capazes de medir e avaliar o nível de desempenho e sucesso econômico e/ou operacional de um dado processo. Além disso, a busca pelo maior lucro através de um melhor consumo de matéria-prima e energia garantindo uma maior qualidade e conformidade de especificações é facilitada pela aplicação de técnicas de otimização em tempo real. Essas técnicas aliadas à KPIs e a controladores preditivos baseado em modelo possibilitam o controle e otimização de sistemas com maior número de variáveis controladas do que manipuladas, sistemas que operam em faixas (também chamadas soft-constraints) e presença de restrições operacionais. No entanto, distúrbios externos não-medidos e a má qualidade de modelos prejudicam o funcionamento robusto do processo levando o sistema a operar fora das especificações, ou em regiões sub-ótimas. Por isso, esta tese aborda um estudo sobre estratégias de otimização em tempo real (Real-time Optimization - RTO) e suas aplicações. As principais contribuições do trabalho são: (1) revisão bibliográfica sobre estratégias de RTO abordando suas principaiscaracterísticas e aplicações; (2) estratégia preliminarde controlador MPC estendido, capaz de abordar controle e otimização em uma única camada; (3) emprego de estimadores de estado e medições do processo para atualização do KPI operacional, considerado uma variável controlada através de set-point pelo controlador MPC; (4) análise da influência do ponto de operação dos modelos utilizados para o KPI e para o controlador MPC linear, estimando-os e atualizando-os através de técnicas de estimação de distúrbios não medidos e parâmetros, baseados em medições do modelo dinâmico não-linear; e (5) influência de fatores de robustez para MPC econômico orientado à KPIs capaz de manter as variáveis controladas através de faixas de operação impondo restrições para as variáveis de entrada e saída do modelo baseado na magnitude do distúrbio. As técnicas propostas são avaliadas e testadas em estudos de caso simulados de forma a exemplificar aplicações industriais corroborando a metodologia proposta ao controlar a função custo no valor ótimo (dado pelo KPI), rejeitando distúrbios e mantendo as saídas do processo nas especificações de forma robusta e sem elevado tempo computacional.Key-performance Indicators (KPIs) are tools capable of measure and evaluate the economic and/or operational development and success of a given process. Besides that, the search for higher operational profit through better consumption of raw materials and energy ensuring greater quality and specifications is facilitated by the application of Real-time Optimization techniques. These techniques allied to KPIs and combined with model-based predictive controllers allow the control and optimization of systems with a greater number of controlled than manipulated variables, systems that operate in ranges (also called softconstraints), and the presence of operational constraints. However, unmeasured external disturbances and poor model quality jeopardize the robust process operation, leading the system to operate outside specifications, or in sub-optimal regions. For this reason, this thesis addresses a study about Real-time Optimization strategies and their applications. The main contributions of this work are (1) bibliographic review about RTO strategies and their main characteristics and applications; (2) preliminary strategy of extended MPC controller, capable of handle control and optimization in one layer; (3) employment of state estimators and process measurements to update the operational KPI, considered as a controlled variable through set-point by the MPC controller; (4) analysis of the operation points of the models employed in the KPI and the linear MPC, estimating and updating them through parameters and unmeasured disturbance estimations techniques, based on measurements of the dynamic nonlinear model; and (5) robustness factors influence for Economic MPC oriented by KPIs, capable of keeping the controlled outputs in ranges and imposing constraints in the inputs and outputs according to the disturbance magnitude. The proposed techniques are evaluated and tested in simulated case studies to exemplify industrial applications corroborating the proposed methodology by controlling the cost function at the optimal value (given by the KPI), rejecting disturbances, and keeping the process outputs in the specifications in a robust way and without higher computational load

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty

Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial application of DRTO is the presence of uncertainty. Many stochastic systems present the following obstacles: 1) plant-model mismatch, 2) process disturbances, 3) risks in violation of process constraints. To accommodate these difficulties, we present a constrained reinforcement learning (RL) based approach. RL naturally handles the process uncertainty by computing an optimal feedback policy. However, no state constraints can be introduced intuitively. To address this problem, we present a chance-constrained RL methodology. We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously. Backoffs are adjusted using the empirical cumulative distribution function to guarantee the satisfaction of a joint chance constraint. The advantage and performance of this strategy are illustrated through a stochastic dynamic bioprocess optimization problem, to produce sustainable high-value bioproducts

Model-based approach for the plant-wide economic control of fluid catalytic cracking unit

Fluid catalytic cracking (FCC) is one of the most important processes in the petroleum refining industry for the conversion of heavy gasoil to gasoline and diesel. Furthermore, valuable gases such as ethylene, propylene and isobutylene are produced. The performance of the FCC units plays a major role on the overall economics of refinery plants. Any improvement in operation or control of FCC units will result in dramatic economic benefits. Present studies are concerned with the general behaviour of the industrial FCC plant, and have dealt with the modelling of the FCC units, which are very useful in elucidating the main characteristics of these systems for better design, operation, and control. Traditional control theory is no longer suitable for the increasingly sophisticated operating conditions and product specifications of the FCC unit. Due to the large economic benefits, these trends make the process control more challenging. There is now strong demand for advanced control strategies with higher quality to meet the challenges imposed by the growing technological and market competition. According to these highlights, the thesis objectives were to develop a new mathematical model for the FCC process, which was used to study the dynamic behaviour of the process and to demonstrate the benefits of the advanced control (particularly Model Predictive Control based on the nonlinear process model) for the FCC unit. The model describes the seven main sections of the entire FCC unit: (1) the feed and preheating system, (2) reactor, (3) regenerator, (4) air blower, (5) wet gas compressor, (6) catalyst circulation lines and (7) main fractionators. The novelty of the developed model consists in that besides the complex dynamics of the reactorregenerator system, it includes the dynamic model of the fractionator, as well as a new five lump kinetic model for the riser, which incorporates the temperature effect on the reaction kinetics; hence, it is able to predict the final production rate of the main products (gasoline and diesel), and can be used to analyze the effect of changing process conditions on the product distribution. The FCC unit model has been developed incorporating the temperature effect on reactor kinetics reference construction and operation data from an industrial unit. The resulting global model of the FCC unit is described by a complex system of partial-differential-equations, which was solved by discretising the kinetic models in the riser and regenerator on a fixed grid along the height of the units, using finite differences. The resulting model is a high order DAE, with 942 ODEs (142 from material and energy balances and 800 resulting from the discretisation of the kinetic models). The model offers the possibility of investigating the way that advanced control strategies can be implemented, while also ensuring that the operation of the unit is environmentally safe. All the investigated disturbances showed considerable influence on the products composition. Taking into account the very high volume production of an industrial FCC unit, these disturbances can have a significant economic impact. The fresh feed coke formation factor is one of the most important disturbances analysed. It shows significant effect on the process variables. The objective regarding the control of the unit has to consider not only to improve productivity by increasing the reaction temperature, but also to assure that the operation of the unit is environmentally safe, by keeping the concentration of CO in the stack gas below a certain limit. The model was used to investigate different control input-output pairing using classical controllability analysis based on relative gain array (RGA). Several multi-loop control schemes were first investigated by implementing advanced PID control using anti-windup. A tuning approach for the simultaneous tuning of multiple interacting PID controllers was proposed using a genetic algorithm based nonlinear optimisation approach. Linear model predictive control (LMPC) was investigated as a potential multi-variate control scheme applicable for the FCCU, using classical square as well as novel non-square control structures. The analysis of the LMPC control performance highlighted that although the multivariate nature of the MPC approach using manipulated and controlled outputs which satisfy controllability criteria based on RGA analysis can enhance the control performance, by decreasing the coupling between the individual low level control loops operated by the higher level MPC. However the limitations of using the linear model in the MPC scheme were also highlighted and hence a nonlinear model based predictive control scheme was developed and evaluated.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Real time optimization of chemical processes

Due to current changes in the global market with increasing competition, strict bounds on product specifications, pricing pressures, and environmental issues, the chemical process industry has a high demand for methods and tools that enhance profitability by reducing the operating costs using limited resources. Real time optimization (RTO) strategies combine process control and economics, and have gone through much advancement during the last few decades. A typical real time optimization application is model based and requires the solution of at least three (usually) nonlinear programming problems, such as combined gross error detection and data reconciliation, parameter estimation and economic optimization. A successful implementation of RTO requires fast and accurate solution of these stated nonlinear programming problems.Current real time optimization strategies wait for steady state after a disturbance enters the process. If, during this wait, another disturbance enters into the system, it will increase the transition time significantly. An alternative, real time evolution (RTE), calculates the new set-points using only disturbance information and the new set-points are implemented in small step changes to a supervisory control system such as model predictive control (MPC) or can be implemented directly to the regulatory control layer. RTE ignores the important part of data screening therefore there is no surety that the calculated set-points represents current plant conditions. The main contribution of this thesis is to investigate the possibility of implementing new set-points without waiting for steady state. Two case studies, the Williams-Otto reactor and an integrated plant (the Williams-Otto reactor extended to include flash drum and large recycle stream), were used for analysis. The application of RTE, RTO and MPC were discussed and compared for the case studies to evaluate the performance in terms of the theoretical profit achieved.A new strategy, dynamic-RTO (D-RTO), based on modified dynamic data reconciliation (DDR) strategy and translated steady state model, was also developed for systems with significant bias and process noise. In the D-RTO strategy, the residual terms of the steady state model were calculated from the reconciled values. These residual terms were translated subsequently into the steady state model. Due to the translation there is no need for calculating set-point changes in small steps. The formulation of the DDR strategy is based on control vector parameterization techniques. D-RTO was compared with RTE and RTO for the two case studies. The results obtained show that RTE can lead to an unstable control if used without taking into account process and controller dynamics. For measurements having bias, the DDR strategy can be used with the assumption that the variables with bias are unmeasured and are calculated implicitly. The D-RTO strategy is able to deal with constant and changing bias, and is able to decrease profit losses during transitions. D-RTO is a good alternative to steady state RTO, for processes with frequent disturbances, where RTO implementation due to its steady state nature may not be justifiable

Implementation and performance assessment of a real-time optimization system on a virtual fluidized-bed catalytic-cracking plant

This thesis develops and evaluates RTO implementation in a FCCU virtual plant, taking into account each RTO stage (noise elimination, steady-state detection, data validation, parameter estimation, and optimization). The dynamic data to carry out this analysis were obtained from an FCCU virtual plant based on a dynamic deterministic model developed in Matlab®. The model output data were contaminated with Gaussian and gross errors to simulate measurements from a real plant. For denoising, steady-state detection, data reconciliation, parameter estimation, and optimization, different strategies and algorithms were studied and assessed, while a decentralized PID was proposed for the control system. Finally, the most appropriate strategies for the case study were implemented and their performance was fully evaluated.Resumen: Esta tesis desarrolla y evalúa la implementación de la RTO en una planta virtual de FCCU, teniendo en cuenta cada etapa de una RTO (eliminación de ruido, detección de estado estable, validación de datos, estimación de parámetros y optimización). Los datos dinámicos para llevar a cabo este análisis se obtuvieron de una planta virtual de FCCU basada en un modelo determinista dinámico desarrollado en Matlab®. Los datos de salida del modelo se contaminaron con error de Gauss y error grueso para simular mediciones de una planta real. Para la eliminación de ruido, la detección de estado estable, la reconciliación de datos, la estimación de parámetros y la optimización, se estudiaron y evaluaron diferentes estrategias y algoritmos, mientras que para el sistema de control se propuso un PID descentralizado. Finalmente, se implementaron las estrategias más apropiadas para el estudio de caso y se evaluó su desempeño en conjunto.Maestrí