Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant

Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems

As renewable energy technologies decrease in cost and become more prevalent, there is an increasing trend towards electrification of many energy systems [...

Exploring the benefits of molten salt reactors: An analysis of flexibility and safety features using dynamic simulation

There has been a growing interest in Molten Salt Reactors (MSRs) in recent years due to the significant potential for increasing flexibility, security, and reliability of the grid, as well as the inherent passive safety features when compared to traditional pressurized water reactors (PWRs). MSRs can help meet many future nuclear energy goals, such as improved sustainability, high security, high efficiency, and high safety passive features, and help reduce nuclear waste. In this study, to investigate MSRs’ passive safety features, a dynamic model of 9 graphite nodes and 18 fuel salt nodes are simulated in 7 safety scenarios. These simulation results are compared with a traditional PWR dynamic simulation. The simulation shows the stability of MSR operations during these 7 safety scenarios, showing that the coolant and graphite temperature within the system stay within the safety limits of operation. The negative feedback coefficient of the fuel salt within MSR cores plays a significant role in stabilizing the power response inside the core, keeping the power from significant excursions. A one-year simulation is also conducted to test the load-following capabilities of MSRs in comparison with traditional PWRs. It is found that MSRs increase the flexibility, reliability, and security of the grid by operating in load-following mode without the need to change the position of the control rods. MSR's increased efficiency also leads to a reduction in backup fossil-fuel based electricity generation by 82% when compared to traditional PWRs operating in load-following mode

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant\u27s NO emission rates is performed, directly comparing each modeling method\u27s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. These efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method\u27s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems

A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs with Complementarity Constraints

This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE) constrained problems where the system may experience an abrupt change in dynamics that may otherwise require a conditional statement, there remain substantial limitations to this methodology, including a limited domain where problems may converge and the possibility for ill-conditioning. Although the problems presented use only continuous algebraic equations, the formulation has inherent non-smoothness. Hence, these problems must be solved with care and only in select circumstances, such as in simulation or situations when the solution is expected to be near the solver’s initial point

Development of novel dynamic machine learning-based optimization of a coal-fired power plant

The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coal-fired boilers now routinely ramp up and down. The current state-of-the-art operation for such boilers is to apply steady-state, neural network-based optimization to make control decisions in real-time, and this work demonstrates the feasibility of extending this to dynamic, neural network-based optimization using a long short-term memory neural network. A simplified numerical simulation of a t-fired coal boiler and supporting equipment is used to represent a real plant subjected to both steady-state, neural network-based optimization and dynamic, neural network-based optimization. Using the same intervals and a particle swarm optimization algorithm, the dynamic optimization outperforms the steady-state optimization and realizes up to 4.58% improvement in thermal efficiency. Dynamic optimization with a long short-term memory neural network is shown to both be feasible and beneficial for operation of a coal-fired boiler under changing load

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. The best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization

Improved Large-Scale Process Cooling Operation through Energy Optimization

This paper presents a study based on real plant data collected from chiller plants at the University of Texas at Austin. It highlights the advantages of operating the cooling processes based on an optimal strategy. A multi-component model is developed for the entire cooling process network. The model is used to formulate and solve a multi-period optimal chiller loading problem, posed as a mixed-integer nonlinear programming (MINLP) problem. The results showed that an average energy savings of 8.57% could be achieved using optimal chiller loading as compared to the historical energy consumption data from the plant. The scope of the optimization problem was expanded by including a chilled water thermal storage in the cooling system. The effect of optimal thermal energy storage operation on the net electric power consumption by the cooling system was studied. The results include a hypothetical scenario where the campus purchases electricity at wholesale market prices and an optimal hour-by-hour operating strategy is computed to use the thermal energy storage tank

A review on the application of machine learning for combustion in power generation applications

Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost