The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and...The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.展开更多
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ...This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.展开更多
Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitatio...Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.展开更多
This paper is standing on the recent viewpoint originated from relevant industrial practices that well or-ganized tracing, representing and feedback(TRF) mechanism of material-flow information is crucial for system ut...This paper is standing on the recent viewpoint originated from relevant industrial practices that well or-ganized tracing, representing and feedback(TRF) mechanism of material-flow information is crucial for system utility and usability of manufacturing execution systems(MES), essentially, for activities on the side of multi-level decision making and optimization mainly in the planning and scheduling. In this paper, we investigate a key issue emphasized on a route of multi-level information evolution on the side of large-scale feedback, where material-flow states could evolve from the measuring data(local states) to networked event-type information cells(global states) and consequently to the key performance indicators(KPI) type information(gross states). Importantly, with adapta-bilities to frequent structural dynamics residing in running material flows, this evolving route should be modeled as a suit of sophisticated mechanism for large-scale dynamic states tracking and representing so as to upgrade accu-racy and usability of the feedback information in MES. To clarify inherent complexities of this evolving route, the investigated issue is demonstrated from extended process systems engineering(PSE) point of view, and the TRF principles of the multi-level feedback information(states) are highlighted under the multi-scale methodology. As the main contribution, a novel mechanism called TRF modeling mechanism is introduced.展开更多
To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much at...To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.展开更多
Integrating marine renewable energy(MRE)with conventional energy sources and logically constructing island energy systems is crucial for alleviating island energy supply challenges and helping coastal energy systems a...Integrating marine renewable energy(MRE)with conventional energy sources and logically constructing island energy systems is crucial for alleviating island energy supply challenges and helping coastal energy systems achieve a sustainable,low-carbon transition.In this study,the status of marine energy utilisation technologies is reviewed,with a focus on advancements in energy conversion equipment,grid integration,and energy storage.The economic feasibility and environmental sustainability of marine energy systems are comparatively analysed to enhance the development and utilisation of marine energy technology while reducing the economic cost of power generation.Suitable equipment is highlighted for islands,with efficient energy generation strategies proposed to achieve cleaner,localised,and cost-effective island integrated energy system(IIES)design.Island energy facilities vary,and integrated development is crucial for building new energy systems.Based on the types and resources of island energy,IIESs are constructed for hierarchical energy utilisation and multi-energy coupling,coordinating resources to achieve source-grid-load-storage integration.The optimisation of IIESs is reviewed,with a focus on modelling methods,intelligent algorithm development,and system simulation.This study differs from previous research as it considers the integration of marine energy into existing systems to achieve comprehensive integration of multiple energy sources.Additionally,optimisation and solution methods for IIES models are summarised.To integrate complex,multivariable energy systems and create stable and predictable outputs,marine energy and load forecasting methods are explored.Overall,this study supports the advancement of marine energy utilisation,focusing on its progressive integration into island energy systems as the efficiency of marine energy improves.This work aims to inspire the development of new functions and modules based on existing system optimisation and forecasting techniques.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.51274110,51304108,U1361211)
文摘The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.
基金supported in part by the Natural Sciences Engineering Research Council of Canada (NSERC)。
文摘This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.
文摘Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.
基金Supported by the National Basic Research Program of China(2012CB720500)the National High Technology Research and Development Program of China(2012AA041102)
文摘This paper is standing on the recent viewpoint originated from relevant industrial practices that well or-ganized tracing, representing and feedback(TRF) mechanism of material-flow information is crucial for system utility and usability of manufacturing execution systems(MES), essentially, for activities on the side of multi-level decision making and optimization mainly in the planning and scheduling. In this paper, we investigate a key issue emphasized on a route of multi-level information evolution on the side of large-scale feedback, where material-flow states could evolve from the measuring data(local states) to networked event-type information cells(global states) and consequently to the key performance indicators(KPI) type information(gross states). Importantly, with adapta-bilities to frequent structural dynamics residing in running material flows, this evolving route should be modeled as a suit of sophisticated mechanism for large-scale dynamic states tracking and representing so as to upgrade accu-racy and usability of the feedback information in MES. To clarify inherent complexities of this evolving route, the investigated issue is demonstrated from extended process systems engineering(PSE) point of view, and the TRF principles of the multi-level feedback information(states) are highlighted under the multi-scale methodology. As the main contribution, a novel mechanism called TRF modeling mechanism is introduced.
基金funding support from the National Natural Science Foundation of China(62394343)the Program of Introducing Talents of Discipline to Universities(the 111 Project)(B17017).
文摘To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.
基金supported by the National Key Research and Development Program of China(2024YFE0100800)the Strategic Research and Consulting Project of the Chinese Academy of Engineering(2024-HZ32)+1 种基金the National Natural Science Foundation of China(52302420)the Major Industry and Technology Project of Zhoushan(2024C03009).
文摘Integrating marine renewable energy(MRE)with conventional energy sources and logically constructing island energy systems is crucial for alleviating island energy supply challenges and helping coastal energy systems achieve a sustainable,low-carbon transition.In this study,the status of marine energy utilisation technologies is reviewed,with a focus on advancements in energy conversion equipment,grid integration,and energy storage.The economic feasibility and environmental sustainability of marine energy systems are comparatively analysed to enhance the development and utilisation of marine energy technology while reducing the economic cost of power generation.Suitable equipment is highlighted for islands,with efficient energy generation strategies proposed to achieve cleaner,localised,and cost-effective island integrated energy system(IIES)design.Island energy facilities vary,and integrated development is crucial for building new energy systems.Based on the types and resources of island energy,IIESs are constructed for hierarchical energy utilisation and multi-energy coupling,coordinating resources to achieve source-grid-load-storage integration.The optimisation of IIESs is reviewed,with a focus on modelling methods,intelligent algorithm development,and system simulation.This study differs from previous research as it considers the integration of marine energy into existing systems to achieve comprehensive integration of multiple energy sources.Additionally,optimisation and solution methods for IIES models are summarised.To integrate complex,multivariable energy systems and create stable and predictable outputs,marine energy and load forecasting methods are explored.Overall,this study supports the advancement of marine energy utilisation,focusing on its progressive integration into island energy systems as the efficiency of marine energy improves.This work aims to inspire the development of new functions and modules based on existing system optimisation and forecasting techniques.
