Experimental Design and Data Processing is an important core professional basic course for food science majors.This course is theoretical and practical,and there are many formulas,abstract contents and difficult to un...Experimental Design and Data Processing is an important core professional basic course for food science majors.This course is theoretical and practical,and there are many formulas,abstract contents and difficult to understand,and there are some problems in the teaching process,such as students1 poor interest in learning,insufficient mastery of what they have learned,and inability to combine theory with practice organically.Through analyzing the existing problems,this paper puts forward some reform measures for the teaching mode of experimental design and data processing by using the intelligent teaching of Superstar platform.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
This work aims to develop a model that will improve the performance and energy efficiency of a novel electrocoagulation(EC)process utilized in wastewater treatment to extrapolate the findings to an industrial scale.Ut...This work aims to develop a model that will improve the performance and energy efficiency of a novel electrocoagulation(EC)process utilized in wastewater treatment to extrapolate the findings to an industrial scale.Utilizing Design of experiments(DOE)allows us to maximize treatment efficiency while minimizing energy consumption.This evaluation was conducted by employing aluminum electrodes as sacrificial anodes.The main factors identified in preliminary experiments are the pH of the medium,the applied potential,and the treatment time.A three-level(3^(3))factorial design was employed to examine the relationship between efficiency performance and energy consumption.Under optimal conditions,treatment efficiency is around 66%for biological oxygen demand within 5 days(BOD_(5)),98%for chemical oxygen demand(COD),associated with a minimum energy consumption of 2.39 kW·h·mg^(-1)of COD.The parameters most significantly influenced by the mathematical models obtained were the potential or applied current,treatment time,and their interaction.The modeling results were also correlated with the experimental results and there were minimal discrepancies.The modeling results were also correlated with the experimental results to assess the accuracy and validity of the model's predictions and there were minimal discrepancies.The results provide promising possibilities for advancing an environmentally friendly wastewater treatment methodology and an economically viable technological solution.展开更多
Food production demand is constantly growing,entailing a proportional increment in fertilisers and pharmaceuticals use,which are eventually introduced to the environment,leading,among others,to an imbalance in the nit...Food production demand is constantly growing,entailing a proportional increment in fertilisers and pharmaceuticals use,which are eventually introduced to the environment,leading,among others,to an imbalance in the nitrogen cycle.Electrochemical nitrate reduction reaction is a delocalised route for nitrates elimination and green ammonia production.In the present study,we carry out nitrates electroreduction over a commercial MoS_(2)catalyst,focusing on optimising selected input factors affecting the reaction.Concretely,Doehlert design of experiment and response surface methodology are employed to find the proper combination of supporting salt concentration in the electrolyte,applied potential,and catalyst loading at the working electrode,with the overall aim to boost Faradaic efficiency(FE)and ammonia production.As a matter of fact,varying these input factors,the obtained FE values ranged from∼2%to∼80%,highlighting the strength of the newly conceived approach.Moreover,our multivariate strategy allows the quantification of each factor effect and elucidates hidden interactions between them.Finally,successful extended durability tests are performed for 100 h at both FE and productivity(P)optimal conditions.In parallel,cell electrodes are characterised by in-depth structural,morphological,and surface techniques,before and after ageing,overall demonstrating the outstanding stability of the proposed electrochemical reactor.展开更多
基金The foundation for Teaching Research Project of Hubei University of Technology in Hubei Province in 2020(grant number 2020017).
文摘Experimental Design and Data Processing is an important core professional basic course for food science majors.This course is theoretical and practical,and there are many formulas,abstract contents and difficult to understand,and there are some problems in the teaching process,such as students1 poor interest in learning,insufficient mastery of what they have learned,and inability to combine theory with practice organically.Through analyzing the existing problems,this paper puts forward some reform measures for the teaching mode of experimental design and data processing by using the intelligent teaching of Superstar platform.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
文摘This work aims to develop a model that will improve the performance and energy efficiency of a novel electrocoagulation(EC)process utilized in wastewater treatment to extrapolate the findings to an industrial scale.Utilizing Design of experiments(DOE)allows us to maximize treatment efficiency while minimizing energy consumption.This evaluation was conducted by employing aluminum electrodes as sacrificial anodes.The main factors identified in preliminary experiments are the pH of the medium,the applied potential,and the treatment time.A three-level(3^(3))factorial design was employed to examine the relationship between efficiency performance and energy consumption.Under optimal conditions,treatment efficiency is around 66%for biological oxygen demand within 5 days(BOD_(5)),98%for chemical oxygen demand(COD),associated with a minimum energy consumption of 2.39 kW·h·mg^(-1)of COD.The parameters most significantly influenced by the mathematical models obtained were the potential or applied current,treatment time,and their interaction.The modeling results were also correlated with the experimental results and there were minimal discrepancies.The modeling results were also correlated with the experimental results to assess the accuracy and validity of the model's predictions and there were minimal discrepancies.The results provide promising possibilities for advancing an environmentally friendly wastewater treatment methodology and an economically viable technological solution.
基金This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 948769, project title: SuN_2rise)the 《HYDREAM》 project–funded by European Union-Next Generation EU–within the PRIN 2022 program (D.D. 104-02/02/2022 Ministero dell’Università e della Ricerca)supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101107906
文摘Food production demand is constantly growing,entailing a proportional increment in fertilisers and pharmaceuticals use,which are eventually introduced to the environment,leading,among others,to an imbalance in the nitrogen cycle.Electrochemical nitrate reduction reaction is a delocalised route for nitrates elimination and green ammonia production.In the present study,we carry out nitrates electroreduction over a commercial MoS_(2)catalyst,focusing on optimising selected input factors affecting the reaction.Concretely,Doehlert design of experiment and response surface methodology are employed to find the proper combination of supporting salt concentration in the electrolyte,applied potential,and catalyst loading at the working electrode,with the overall aim to boost Faradaic efficiency(FE)and ammonia production.As a matter of fact,varying these input factors,the obtained FE values ranged from∼2%to∼80%,highlighting the strength of the newly conceived approach.Moreover,our multivariate strategy allows the quantification of each factor effect and elucidates hidden interactions between them.Finally,successful extended durability tests are performed for 100 h at both FE and productivity(P)optimal conditions.In parallel,cell electrodes are characterised by in-depth structural,morphological,and surface techniques,before and after ageing,overall demonstrating the outstanding stability of the proposed electrochemical reactor.
