A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU w...A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.展开更多
In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-...In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-EDR)was initially used to construct a first principle model(FP)of the STHE using industrial data.The Genetic Algorithm(GA)was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions.A dataset comprised of optimum process conditions was generated through FP-GA integration and was utilised to develop an Artificial Neural Networks(ANN)model.Subsequently,the ANN model was merged with the FP model by substituting the GA,to form a GB model.The developed GB model,that is,ANN and FP integration,achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model.Performance of the GB framework was also comparable to the FP-GA approach but it significantly reduced the computation time required for estimating the optimum process conditions.The proposed GB-based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.展开更多
We presented a control strategy for tablet manufacturing processes based on continuous direct compression.The work was conducted by the experts of pharmaceutical companies,machine suppliers,academia,and regulatory aut...We presented a control strategy for tablet manufacturing processes based on continuous direct compression.The work was conducted by the experts of pharmaceutical companies,machine suppliers,academia,and regulatory authority in Japan.Among different items in the process,the component ratio and blended powder content were selected as the items requiring the control method specific to continuous manufacturing different from the conventional batch manufacturing.The control and management of the Loss in Weight(LIW)feeder were deemed the most important,and the Residence Time Distribution(RTD)model were regarded effective for setting the control range and for controlling of the LIW feeder.Based on these ideas,the concept of process control using RTD was summarized.展开更多
Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to ...Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes.A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of±5%in process conditions,such as temperature,flow rates,etc.The generated data was used to train support vector machines(SVM),Gaussian process regression(GPR),artificial neural networks(ANN),regression trees(RT),and ensemble trees(ET).Hyperparameter tuning was performed to enhance the prediction capabilities of GPR,ANN,SVM,ET and RT models.Performance analysis of the models indicates that GPR,ANN,and SVM with R2 values of 0.99,0.978,and 0.979 and RMSE values of 0.108,0.262,and 0.258,respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables.ET and RT had an R2 value of 0.94 and 0.89,respectively.The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method.Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%.The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.展开更多
The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on ...The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.展开更多
The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furn...The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.展开更多
基金Higher Education Commission,Pakistan,under the National Research Program for Universities Project,Grant/Award Number:NBU-FPEJ-2024-1243-02。
文摘A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.
基金National Research Program for Universities,Grant/Award Number:10215/FederalNorthern Border University,Arar,KSA,Grant/Award Number:NBU-FPEJ-2024-1243-01。
文摘In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-EDR)was initially used to construct a first principle model(FP)of the STHE using industrial data.The Genetic Algorithm(GA)was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions.A dataset comprised of optimum process conditions was generated through FP-GA integration and was utilised to develop an Artificial Neural Networks(ANN)model.Subsequently,the ANN model was merged with the FP model by substituting the GA,to form a GB model.The developed GB model,that is,ANN and FP integration,achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model.Performance of the GB framework was also comparable to the FP-GA approach but it significantly reduced the computation time required for estimating the optimum process conditions.The proposed GB-based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.
文摘We presented a control strategy for tablet manufacturing processes based on continuous direct compression.The work was conducted by the experts of pharmaceutical companies,machine suppliers,academia,and regulatory authority in Japan.Among different items in the process,the component ratio and blended powder content were selected as the items requiring the control method specific to continuous manufacturing different from the conventional batch manufacturing.The control and management of the Loss in Weight(LIW)feeder were deemed the most important,and the Residence Time Distribution(RTD)model were regarded effective for setting the control range and for controlling of the LIW feeder.Based on these ideas,the concept of process control using RTD was summarized.
基金Higher Education Commission(HEC),Pakistan,under the National Research Program for Universities(NRPU)Project No.10,215/Federal.
文摘Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes.A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of±5%in process conditions,such as temperature,flow rates,etc.The generated data was used to train support vector machines(SVM),Gaussian process regression(GPR),artificial neural networks(ANN),regression trees(RT),and ensemble trees(ET).Hyperparameter tuning was performed to enhance the prediction capabilities of GPR,ANN,SVM,ET and RT models.Performance analysis of the models indicates that GPR,ANN,and SVM with R2 values of 0.99,0.978,and 0.979 and RMSE values of 0.108,0.262,and 0.258,respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables.ET and RT had an R2 value of 0.94 and 0.89,respectively.The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method.Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%.The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.
文摘The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.
基金Project supported by the National Natural Science Founda-tion of China(Nos.62003301,61933013,and 61833014)the Natural Science Foundation of Zhejiang Province,China(No.LQ21F030018)the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang Univer-sity,China(Nos.ICT2022B30 and ICT2022B08)。
文摘The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.
