title: Intelligent knowledge-based control system of industrial steam boiler
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÐ¼ Ð¿Ð°ÑÐ¾Ð²ÑÐ¼ ÐºÐ¾ÑÐ»Ð¾Ð¼
Creators: name: ÐÐ¸Ð±, ÐÐ»Ð¸
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ°ÑÐ¾Ð²ÑÐµ ÐºÐ¾ÑÐ»Ñ
control
optimization
ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ

description: The goal of this research is to address the problem of controlling and optimizing industrial thermal power plants, focusing on boiler-turbine systems. We introduce Machine Learning strategies for identification and control of the boiler and optimize a Combined Heat and Power (CHP) unit using Pareto Front optimization. This research employs a hierarchical control strategy applying Model Predictive Control (MPC) with Evolutionary Machine Learning to improve control without the need for linearization. Genetic Algorithms (GAs) guide a multi-goal control strategy for nonlinear systems with dynamic constraints. Online data-driven identification using Neural Networks is utilized for system identification, with error compensation from past iterations aiding in the training process. Generative Adversarial Networks (GANs) reduce the search space dimensions of the GAs, and hierarchical non-dominated sorting is used to find the optimal system configurations. One main result is our proposed hierarchical control strategy that applies MPC with Evolutionary Machine Learning to improve control without the need for linearization. Specifically, our strategy leverages GAs to guide a multi-goal control strategy for nonlinear systems with dynamic constraints. Our findings demonstrate the ability of GAs to efficiently provide control values and show that our framework can overcome the computational complexity of GAs, allowing real-time search and control through our proposed multi-level strategy. We also considered online data-driven identification using Neural Networks for system identification. Our findings show that compensating the error from past iterations helps overcome the slow convergence of the neural network training process, leading to proper online-learning through the NN model. This model is then used as the nonlinear system to be controlled by the previously suggested GA-based control framework. Furthermore, due to the exponential increase in computational complexity of GAs with search space dimensions (known as the curse of dimensionality), we discuss reducing the search space of the Genetic Algorithm through GANs. We also address optimizing production effectiveness by analyzing suitable configuration parameter ranges for desired production outcomes. Finally, a computer software was developed and tested using real-world data from a large industrial Russian company, demonstrating the practical applicabilityand effectiveness of the proposed methods. By addressing the severe non-linearity and complex interactions within boiler-turbine systems, this research offers significant theoretical and practical advancements, paving the way for more efficient and reliable thermal power generation. Integrating advanced control strategies, including data-driven optimization and machine learning, can significantly enhance the performance and reliability of boiler-turbine systems, ensuring efficient and sustainable energy production. The main application areas of this work are thermal power plants and energy systems. The software developed is beneficial for controlling and optimizing industrial boiler systems. The hierarchical control strategy can be applied as a universal model-based multi-objective control strategy for nonlinear systems.
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