The enhancement of chemical absorption of CO2 by K2CO3/H2O absorbents in the presence of activated carbon （AC） particles was investigated. The results show that the gas absorption rates can be enhanced significantly...The enhancement of chemical absorption of CO2 by K2CO3/H2O absorbents in the presence of activated carbon （AC） particles was investigated. The results show that the gas absorption rates can be enhanced significantly in the presence of AC particles, and the maximum enhancement factor 3.7 was observed at low stirring intensities. The enhancement factor increased rapidly with the solid loading during the initial period of absorption and then be- came mild gradually to a maximum value. Both the liquid-solid contact area and the probability of solid particles residing at the gas-liquid interface decreased with the increase of the particle size, leading to a negative effect on the enhancement of mass transfer. The influence of the particles on gas absorption decreased with the reaction rate. The stirring speed changed the interfacial coverage and mass transfer rate on the liquid side and consequently affected the mass transfer between the gas and liquid phases; the enhancement factor decreased with the stirring intensity. A heterogeneous two-zone model was proposed for predicting the enhancement factor and the calculated results agreed well with the experimental data.展开更多
A conventional diet based on corn and soybean meal fed to pigs is usually provided in a mash form and in most cases, processing other than grinding and mixing is not used. However, due to the high cost of energy in pi...A conventional diet based on corn and soybean meal fed to pigs is usually provided in a mash form and in most cases, processing other than grinding and mixing is not used. However, due to the high cost of energy in pig diets,use of high fiber ingredients such as soybean hulls, distillers dried grains with solubles, and wheat middlings has increased. High fiber concentrations in the diet usually results in reduced energy and nutrient digestibility due to the low capacity of pigs to digest fiber, which negatively impacts growth performance and carcass composition of the pigs. Feed processing technologies such as changes in grinding procedures, expansion, extrusion, pelleting, use of enzymes or chemical treatments may, however, be used to solubilize some of the cel ulose and hemicel ulose fractions that form the cel wal of plants in the ingredients, and therefore, increase nutrient availability. This may have a positive effect on energy digestibility, and therefore, also on pig growth performance and carcass composition, but effects of different feed technologies on the nutritional value of feed ingredients and diets fed to pigs are not fully understood. It has however, been demonstrated that reduced particle size of cereal grains usually results in increased digestibility of energy, primarily due to increased digestibility of starch. Extrusion or expansion of ingredients or diets may also increase energy digestibility and it appears that the increase is greater in high fiber diets than in diets with lower concentrations of fiber. Chemical treatments have not consistently improved energy or nutrient digestibility, but a number of different enzymes may be used to increase the digestibility of phosphorus, calcium, or energy. Thus, there are several opportunities for using feed technology to improve the nutritional value of diets fed to pigs.展开更多
The particle characterization from the influent and effluent of a chemical-biological flocculation （CBF） process was studied with a laser diffraction device. Water samples from a chemically enhanced primary treatme...The particle characterization from the influent and effluent of a chemical-biological flocculation （CBF） process was studied with a laser diffraction device. Water samples from a chemically enhanced primary treatment （CEPT） process and a primary sediment tank process were also analyzed for comparison. The results showed that CBF process was not only effective for both the big size particles and small size particles removal, but also the best particle removal process in the three processes. The results also indicated that CBF process was superior to CEPT process in the heavy metals removal. The high and non-selective removal for heavy metals might be closely related to its strong ability to eliminate small particles. Samples from different locations in CBF reactors showed that small particles were easier to aggregate into big ones and those disrupted flocs could properly flocculate again along CBF reactor because of the biological flocculation.展开更多
Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactio...Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.展开更多
The mining-beneficiation wastewater treatment is highly complex and nonlinear.Various factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation was...The mining-beneficiation wastewater treatment is highly complex and nonlinear.Various factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater treatment.Chemical oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation wastewater.Predicting COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant discharge.This reduces environmental risk and significantly improves the discharge quality of wastewater.This paper presents a novel AI algorithm PSO-SVR,to predict water quality.Hyperparameter optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD prediction.The generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is 0.85.We compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation wastewater.This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures.Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment.In addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.展开更多
基金Supported by the National Natural Science Foundation of China （No.20176036）.
文摘The enhancement of chemical absorption of CO2 by K2CO3/H2O absorbents in the presence of activated carbon （AC） particles was investigated. The results show that the gas absorption rates can be enhanced significantly in the presence of AC particles, and the maximum enhancement factor 3.7 was observed at low stirring intensities. The enhancement factor increased rapidly with the solid loading during the initial period of absorption and then be- came mild gradually to a maximum value. Both the liquid-solid contact area and the probability of solid particles residing at the gas-liquid interface decreased with the increase of the particle size, leading to a negative effect on the enhancement of mass transfer. The influence of the particles on gas absorption decreased with the reaction rate. The stirring speed changed the interfacial coverage and mass transfer rate on the liquid side and consequently affected the mass transfer between the gas and liquid phases; the enhancement factor decreased with the stirring intensity. A heterogeneous two-zone model was proposed for predicting the enhancement factor and the calculated results agreed well with the experimental data.
文摘A conventional diet based on corn and soybean meal fed to pigs is usually provided in a mash form and in most cases, processing other than grinding and mixing is not used. However, due to the high cost of energy in pig diets,use of high fiber ingredients such as soybean hulls, distillers dried grains with solubles, and wheat middlings has increased. High fiber concentrations in the diet usually results in reduced energy and nutrient digestibility due to the low capacity of pigs to digest fiber, which negatively impacts growth performance and carcass composition of the pigs. Feed processing technologies such as changes in grinding procedures, expansion, extrusion, pelleting, use of enzymes or chemical treatments may, however, be used to solubilize some of the cel ulose and hemicel ulose fractions that form the cel wal of plants in the ingredients, and therefore, increase nutrient availability. This may have a positive effect on energy digestibility, and therefore, also on pig growth performance and carcass composition, but effects of different feed technologies on the nutritional value of feed ingredients and diets fed to pigs are not fully understood. It has however, been demonstrated that reduced particle size of cereal grains usually results in increased digestibility of energy, primarily due to increased digestibility of starch. Extrusion or expansion of ingredients or diets may also increase energy digestibility and it appears that the increase is greater in high fiber diets than in diets with lower concentrations of fiber. Chemical treatments have not consistently improved energy or nutrient digestibility, but a number of different enzymes may be used to increase the digestibility of phosphorus, calcium, or energy. Thus, there are several opportunities for using feed technology to improve the nutritional value of diets fed to pigs.
基金Project supported by the Hi-Tech Research and Development Program (863) of China (No. 2002AA601320) the Shandong Environment Protection Bureau Program (No. 2006032, 2006043)the Ph.D Fund of Shandong Jianzhu University (No. 624006, 2006043).
文摘The particle characterization from the influent and effluent of a chemical-biological flocculation （CBF） process was studied with a laser diffraction device. Water samples from a chemically enhanced primary treatment （CEPT） process and a primary sediment tank process were also analyzed for comparison. The results showed that CBF process was not only effective for both the big size particles and small size particles removal, but also the best particle removal process in the three processes. The results also indicated that CBF process was superior to CEPT process in the heavy metals removal. The high and non-selective removal for heavy metals might be closely related to its strong ability to eliminate small particles. Samples from different locations in CBF reactors showed that small particles were easier to aggregate into big ones and those disrupted flocs could properly flocculate again along CBF reactor because of the biological flocculation.
基金This work was supported by the“Zhujiang Talent Program”High Talent Project of Guangdong Province(Grant No.2017GC010614)the National Natural Science Foundation of China(Grant No.22078372).
文摘Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.
基金supported by European Social Fund via IT Academy program,the Science and Technology Program of Guangdong Forestry Administration(China)(No.2020-KYXM-08)the Major Science and Technology Program for Water Pollution Control and Treatment(China)(No.2017ZX07101003)+1 种基金National Key Research and Development Project(China)(No.2019YFC1804800)Pearl River S&T Nova Program of Guangzhou,China(No.201710010065).
文摘The mining-beneficiation wastewater treatment is highly complex and nonlinear.Various factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater treatment.Chemical oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation wastewater.Predicting COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant discharge.This reduces environmental risk and significantly improves the discharge quality of wastewater.This paper presents a novel AI algorithm PSO-SVR,to predict water quality.Hyperparameter optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD prediction.The generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is 0.85.We compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation wastewater.This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures.Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment.In addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.
