Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several k...
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Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in *** paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up *** with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large *** methods applied in product yield analysis build models based on massive *** fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model *** methods applied in flue gas desulfurization can be usually divided into two stages:modeling and *** the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule *** methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition ***-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction *** the data-driven methods are just the ***,some studies combine these two methods to obtain better results.
Background:Fluorescence microscopy has increasingly promising applications in life *** bibliometrics-based review focuses on deep learning assisted fluorescence microscopy imaging ***:Papers on this topic retrieved by...
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Background:Fluorescence microscopy has increasingly promising applications in life *** bibliometrics-based review focuses on deep learning assisted fluorescence microscopy imaging ***:Papers on this topic retrieved by Core Collection on Web of Science between 2017 and July 2022 were used for the *** addition to presenting the representative papers that have received the most attention,the process of development of the topic,the structure of authors and institutions,the selection of journals,and the keywords are analyzed in detail in this ***:The analysis found that this topic gained immediate popularity among scholars from its emergence in 2017,gaining explosive growth within three *** phenomenon is because deep learning techniques that have been well established in other fields can be migrated to the analysis of fluorescence *** 2020 onwards,this topic tapers off but has attracted a few stable research groups to tackle the remaining *** this topic has been very popular,it has not attracted scientists from all over the *** USA,China,Germany,and the UK are the key players in this *** analysis and clustering are applied to understand the different focuses on this ***:Based on the bibliometric analysis,the current state of this topic to date and future perspectives are summarized at the end.
Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most exis...
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Solving the Economic and Emission dispatch (ED/MED) problem becomes more complex when the combined version (CEED) of the two aforementioned cases is considered. The basic idea is to achieve the lowest possible cost wi...
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ISBN:
(纸本)9781509006243
Solving the Economic and Emission dispatch (ED/MED) problem becomes more complex when the combined version (CEED) of the two aforementioned cases is considered. The basic idea is to achieve the lowest possible cost with the smallest amount of pollutant and this problem is known as the combined economic-emission dispatch (CEED). In this paper we make use of a multi-objective optimization approach in order to find the best balance when the CEED is considered. Another contribution of this article is the incorporation of what is known as the autonomous energy generating system, which is composed by Micro-turbines, Wind, Solar, and Battery generators; they are used along with the thermal (diesel) generating units. To find the best cost-emission balance in the objective function, we've implemented two meta-heuristic optimization tools, the well-known Harmony Search and a novel one named Virus Optimization algorithms, HS and VOA respectively. The study was performed on a system composed of 15 generating units. Here, a thorough statistical study was conducted in order to determine not only the performance of the two optimization tools in term of computation efficiency, but also, the quality of the Pareto Frontier delivered by both algorithms, which are compared with a Reference Pareto Front (RPF). Based on this study we found that both algorithms delivered competing results, with the Harmony Search having slightly better Euclidean distance value from the RPF when compared with the Virus Optimization algorithm; however, HS does take much longer computation time to achieve this result.
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