Data-driven Scheduling Optimization of Ethylene Cracking Furnace System

Lin, Xinwei; Zhao, Liang; Du, Wenli; Qian, Feng

doi:10.1109/ccdc49329.2020.9164201

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…These accomplishments provide valuable guidance and support for the collaborative optimization of the entire process in the MSWI process. In the field of intelligent optimization decision making, studies on the intelligent decision making of the entire process operation in mineral processing [226], scheduling optimization of the ethylene cracking furnace system [227], multifurnace optimal scheduling of silicon single crystal and fused magnesium production processes [228,229], and others have been successfully conducted. These achievements underscore that research on multi-objective real-time optimization algorithms and human-machine collaborative enhanced interactive evolution for intelligent decision making in the MSWI process has a solid theoretical foundation and practical feasibility.…”

Section: Collaborative Optimization Of Whole Processmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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Section: Collaborative Optimization Of Whole Processmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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“…The feed and dilution steam flow to the convection section for preheating and then flow to the radiant coil for thermal cracking reactions. After the pyrolysis reaction in the coil, the cracking gas mixture flows into the quench section, and it is quenched to near 250 °C to prevent secondary reactions . Multistage compression is employed to further compress the cracking gas mixture to remove acid components from the gas.…”

Section: Introductionmentioning

confidence: 99%

Integrated Modeling and Optimization for Ethylene Production Processes Coupled with the Utility System

Ma,

Zhao,

Yang

2024

Ind. Eng. Chem. Res.

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Ethylene production is primarily concerned with improving economic benefits. Modeling and optimization of ethylene production processes are challenging due to their complexity and coupling with the utility system. In existing approaches, utility system interactions are not considered, which could result in suboptimal solutions. This paper presents a new modeling and optimization framework for integrating production processes with utility systems by recovering superhigh-pressure steam from the cracking process. A data-driven model based on industrial and software-generated data is used in conjunction with an existing mechanism model for simplifying complex process engineering and reducing the optimization difficulty. The case study shows that this methodology increases the profit by 5%. Sensitivity analysis confirms sufficient stability to withstand market fluctuations.

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An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Preprint

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Artificial intelligence (AI) has found extensive applications in the sustainability domain. The municipal solid waste incineration (MSWI) process can achieve waste-to-energy (WTE) goals through the assistance of automation and information technologies. In theory, AI can facilitate the intelligent operation of the MSWI process, simultaneously achieving low pollution emissions and high production efficiency. This study provides an overview of AI applications in the MSWI process. Addressing fundamental challenges, the optimal control of the MSWI process is categorized into four aspects: modeling, control, optimization, and maintenance. Subsequently, this study conducts a detailed analysis and discussion of AI applications in each of these technological domains. In conclusion, this paper outlines the future research directions and provides an outlook on the fundamental issues pertaining to AI applications in the optimal control of the MSWI process.

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Data-driven Scheduling Optimization of Ethylene Cracking Furnace System

Cited by 3 publications

References 15 publications

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Integrated Modeling and Optimization for Ethylene Production Processes Coupled with the Utility System

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

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