Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Symmetry in Renewable Energy and Power Systems

This book includes original research papers related to renewable energy and power systems in which theoretical or practical issues of symmetry are considered. The book includes contributions on voltage stability analysis in DC networks, optimal dispatch of islanded microgrid systems, reactive power compensation, direct power compensation, optimal location and sizing of photovoltaic sources in DC networks, layout of parabolic trough solar collectors, topologic analysis of high-voltage transmission grids, geometric algebra and power systems, filter design for harmonic current compensation. The contributions included in this book describe the state of the art in this field and shed light on the possibilities that the study of symmetry has in power grids and renewable energy systems

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

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

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