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Groundbreaking control method reduces carbon emissions from zinc oxide rotary kilns, boosting profits for zinc smelting industry
IMAGE: SCHEMATIC OF THE PROPOSED A MULTI-OBJECTIVE ADAPTIVE OPTIMIZATION MODEL PREDICTIVE CONTROL METHOD. view more
CREDIT: KE WEI ET AL.
A research team from Central South University in China develops innovative control method to reduce carbon emissions from zinc oxide rotary kilns.
The zinc smelting industry is facing new challenges in meeting China’s carbon peak and carbon neutrality targets. To address these challenges, researchers from Central South University in China have developed a groundbreaking control method that reduces carbon emissions from zinc oxide rotary kilns while maintaining high profits. Their findings have been published in the journal Engineering.
Zinc oxide rotary kilns play a crucial role in the zinc smelting process. However, traditional stability control methods are no longer suitable for the industry’s multi-objective control tasks. In their paper, Keke Huang’s research team proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification.
The researchers first formulated and solved a sparse regression problem to obtain a reduction model using a large amount of data collected from a computational fluid dynamics (CFD) simulation model. This reduction model overcomes the high computational complexity of traditional CFD models, enabling real-time computation of the zinc oxide rotary kiln dynamics.
The proposed control method consists of a two-layered framework: real-time optimization (RTO) and model predictive control (MPC). In the RTO layer, an optimization problem is set up to achieve optimal operation performance and the lowest possible resource consumption. By solving this problem in real time, an optimal setting value is sent to the MPC layer, ensuring that the zinc oxide rotary kiln always operates in an optimal state.
The experiments conducted by the research team demonstrate the strength and reliability of the proposed method. It not only reduces the usage of coal but also maintains high profits for the industry. The control method offers a promising solution for zinc smelting companies to meet China’s carbon reduction goals and contribute to a greener future.
Nan Zhang, editor of the subject of chemical, metallurgical, and materials engineering of Engineering, commented, “This MAO-MPC method provides an effective approach for reducing carbon emissions from zinc oxide rotary kilns. By optimizing the process in real time, the zinc smelting industry can achieve significant reductions in coal consumption while maintaining high profits. This research has the potential to revolutionize the zinc smelting industry and contribute to China’s carbon neutrality goals.”
The research team’s work opens up avenues for further study, including improving the accuracy of first-principles models and designing optimization objective functions to enhance the performance of rotary kilns.
The paper “Multi-Objective Adaptive Optimization Model Predictive Control: Decreasing Carbon Emissions from a Zinc Oxide Rotary Kiln”, authored by Ke Wei, Keke Huang, Chunhua Yang, Weihua Gui. Full text of the open access paper: https://doi.org/10.1016/j.eng.2023.01.017. For more information about the Engineering, follow us on Twitter (https://twitter.com/EngineeringJrnl) & like us on Facebook (https://www.facebook.com/EngineeringPortfolio).
About Engineering
Engineering (ISSN: 2095-8099 IF:12.8) is an international open-access journal that was launched by the Chinese Academy of Engineering (CAE) in 2015. Its aims are to provide a high-level platform where cutting-edge advancements in engineering R&D, current major research outputs, and key achievements can be disseminated and shared; to report progress in engineering science, discuss hot topics, areas of interest, challenges, and prospects in engineering development, and consider human and environmental well-being and ethics in engineering; to encourage engineering breakthroughs and innovations that are of profound economic and social importance, enabling them to reach advanced international standards and to become a new productive force, and thereby changing the world, benefiting humanity, and creating a better future.
JOURNAL
Engineering
ARTICLE TITLE
Multi-Objective Adaptive Optimization Model Predictive Control: Decreasing Carbon Emissions from a Zinc Oxide Rotary Kiln
An intelligent control method reduces carbon emissions in energy-intensive equipment
IMAGE: STRUCTURE OF THE INTELLIGENT CONTROL METHOD FOR LOW-CARBON OPERATION. view more
CREDIT: TIANYOU CHAI ET AL.
A research team led by Professor Tianyou Chai from Northeastern University, China, has developed an innovative intelligent control method for the low-carbon operation of energy-intensive equipment. This groundbreaking research, published in the journal Engineering, presents a significant step towards reducing carbon emissions in the process industry.
The research team’s method combines mechanism analysis with deep learning, linking control and optimization with prediction, and integrating decision-making with control. By employing setpoint control, self-optimized tuning, and tracking control, the method ensures that the energy consumption per tonne is minimized while remaining within the target range.
The intelligent control system developed by adopting the end–edge–cloud collaboration technology of the Industrial Internet has been successfully applied to a fused magnesium furnace, yielding remarkable results in reducing carbon emissions. The CO2 emissions were reduced by an impressive 8.82%, while simultaneously increasing the yield of high-quality products by 3.65% and reducing electrode consumption by 3.73%.
The setpoint control component of the method includes a tracking control presetting model, a prediction model of energy consumption per tonne, a feedforward compensator, and a feedback compensator. The self-optimized tuning component involves operating condition recognition, an intelligent prediction model of energy consumption per tonne, and a self-tuning compensator. Lastly, the tracking control adopts an adaptive proportional–integral–derivative (PID) controller based on signal compensation.
While the research team celebrates these achievements, they acknowledge that further challenges lie ahead. The development of a modeling method based on digital twin technology, optimal decision-making of the setpoint for process control with conflicting objectives, and controller parameter optimization of a high-performance control system are among the challenges that require attention.
To realize the low-carbon operational control of complex industrial systems, the research team emphasizes the need for further study in several areas. These include developing a modeling method based on digital twins for complex production processes by combining mechanism analysis with deep learning, creating a method for high-performance control systems by combining digital twins with machine learning, establishing a low-carbon operational control method for complex industrial systems based on the industrial metaverse, and implementing end–edge–cloud collaboration technology to realize low-carbon operational control.
Professor Chai and his team’s research opens up a new path towards achieving low-carbon operational control in the process industry. By combining cutting-edge technologies and innovative approaches, they have demonstrated the potential for significant reductions in carbon emissions while maintaining optimal operational efficiency.
Nan Zhang, editor of the subject of chemical, metallurgical, and materials engineering of Engineering, commented, “This research has far-reaching implications for the future of sustainable industrial practices. As the world continues to grapple with the challenges of climate change, the intelligent control method developed by Professor Chai’s team presents a promising solution for reducing carbon emissions in energy-intensive equipment.”
The paper “An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment”, authored by Tianyou Chai, Mingyu Li, Zheng Zhou, Siyu Cheng, Yao Jia, Zhiwei Wu. Full text of the open access paper: https://doi.org/10.1016/j.eng.2023.05.018. For more information about the Engineering, follow us on Twitter (https://twitter.com/EngineeringJrnl) & like us on Facebook (https://www.facebook.com/EngineeringPortfolio).
About Engineering
Engineering (ISSN: 2095-8099 IF:12.8) is an international open-access journal that was launched by the Chinese Academy of Engineering (CAE) in 2015. Its aims are to provide a high-level platform where cutting-edge advancements in engineering R&D, current major research outputs, and key achievements can be disseminated and shared; to report progress in engineering science, discuss hot topics, areas of interest, challenges, and prospects in engineering development, and consider human and environmental well-being and ethics in engineering; to encourage engineering breakthroughs and innovations that are of profound economic and social importance, enabling them to reach advanced international standards and to become a new productive force, and thereby changing the world, benefiting humanity, and creating a better future.
JOURNAL
Engineering
ARTICLE TITLE
An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment
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