In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of M...In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine(ELM) and modified gravitational search algorithm(MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.展开更多
This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measur...This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial processes.Lack of online measurement instruments becomes a problem in polymer quality control.One effective solution is to use soft sensors to estimate the quality variables from process data.In recent years,deep learning has achieved many successful applications in image classification and speech recognition.DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture.It can meet the demand of modelling accuracy when applied to actual processes.Compared to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training phase.To mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation.Selection of DBN structure is investigated in the paper.The modelling results achieved by DBN and feedforward neural networks are compared in this paper.It is shown that the DBN models give very accurate estimations of the polymer melt index.展开更多
Herein, a multi-index analysis of the nickel content of an alloy, output rate of the alloy, nickel recovery rate, and iron recovery rate during the melting of laterite metallized pellets was performed. The thermodynam...Herein, a multi-index analysis of the nickel content of an alloy, output rate of the alloy, nickel recovery rate, and iron recovery rate during the melting of laterite metallized pellets was performed. The thermodynamic reduction behavior of oxides such as NiO, FeO, Fe_3 O_4, and Cr_2 O_3 was studied using the FactSage software, which revealed that SiO_2 is not conducive to the reduction of iron oxides, whereas the addition of basic oxides such as CaO and MgO is beneficial for the reduction of iron oxides. On the basis of a comprehensive analysis to achieve greater nickel recovery and lower iron recovery rates, the optimum experimental parameters in the orthogonal experiment were A3 B1 C3(t = 30 min, C/O = 0.4, R = 1.2); the indicators wNi, φalloy, ηNi, and ηFe had values of 15.0 wt%, 12.1%, 44.9%, and 96.4%, respectively. In single-factor experiments, increasing basicity(R) substantially improved the separation effect in the low-basicity range 0.5 ≤ R ≤ 0.8 but not in the high-basicity range 0.8 ≤ R ≤ 1.2. Similar results were obtained for the effect of the C/O ratio. Moreover, the recovery rate of nickel increased with increasing recovery rate of iron.展开更多
The aim of this study was to formulate and develop a low calorie and low glycemic index (GI) of soft ice cream by using mixture of sucrose and Stevia. Five different formulations of ice cream were produced by using di...The aim of this study was to formulate and develop a low calorie and low glycemic index (GI) of soft ice cream by using mixture of sucrose and Stevia. Five different formulations of ice cream were produced by using different proportions of sucrose and Stevia. Physicochemical characteristics, hedonic sensory evaluations and glycemic index determination of products were carried out by following conventional methods. Replacement of sucrose with Stevia resulted in a significantly lower viscosity and brix with a higher overrun and melting rate in a dose dependent manner. Total replacing of sucrose with Stevia resulted in significant reduction in caloric value from 143.03 to 105.25 Kcal and GI from 79.06 ± 4.0 to 72.18 ± 5.27 as compared to those of sucrose based formulation (p 0.05) indicating a 37.78% and 6.88% reduction, respectively. TB had the best sensory acceptance among all the treatments. We concluded that substitution of sucrose with Stevia may be a choice to produce low caloric and GI ice creams. However, using mixture of the two sweeteners improves sensory acceptance of the formulations.展开更多
基金Supported by the Major Program of National Natural Science Foundation of China(61590921)the Natural Science Foundation of Zhejiang Province(Y16B040003)+1 种基金Shanghai Aerospace Science and Technology Innovation Fund(E11501)Aerospace Science and Technology Innovation Fund of China,Aerospace Science and Technology Corporation(E11601)
文摘In propylene polymerization(PP) process, the melt index(MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine(ELM) and modified gravitational search algorithm(MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.
基金supported by National Natural Science Foundation of China (No. 61673236)the European Union (No. PIRSES-GA-2013-612230)
文摘This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial processes.Lack of online measurement instruments becomes a problem in polymer quality control.One effective solution is to use soft sensors to estimate the quality variables from process data.In recent years,deep learning has achieved many successful applications in image classification and speech recognition.DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture.It can meet the demand of modelling accuracy when applied to actual processes.Compared to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training phase.To mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation.Selection of DBN structure is investigated in the paper.The modelling results achieved by DBN and feedforward neural networks are compared in this paper.It is shown that the DBN models give very accurate estimations of the polymer melt index.
基金financially supported by the National Natural Science Foundation of China (Nos. 51474024, 51674021, and 51574021)
文摘Herein, a multi-index analysis of the nickel content of an alloy, output rate of the alloy, nickel recovery rate, and iron recovery rate during the melting of laterite metallized pellets was performed. The thermodynamic reduction behavior of oxides such as NiO, FeO, Fe_3 O_4, and Cr_2 O_3 was studied using the FactSage software, which revealed that SiO_2 is not conducive to the reduction of iron oxides, whereas the addition of basic oxides such as CaO and MgO is beneficial for the reduction of iron oxides. On the basis of a comprehensive analysis to achieve greater nickel recovery and lower iron recovery rates, the optimum experimental parameters in the orthogonal experiment were A3 B1 C3(t = 30 min, C/O = 0.4, R = 1.2); the indicators wNi, φalloy, ηNi, and ηFe had values of 15.0 wt%, 12.1%, 44.9%, and 96.4%, respectively. In single-factor experiments, increasing basicity(R) substantially improved the separation effect in the low-basicity range 0.5 ≤ R ≤ 0.8 but not in the high-basicity range 0.8 ≤ R ≤ 1.2. Similar results were obtained for the effect of the C/O ratio. Moreover, the recovery rate of nickel increased with increasing recovery rate of iron.
文摘The aim of this study was to formulate and develop a low calorie and low glycemic index (GI) of soft ice cream by using mixture of sucrose and Stevia. Five different formulations of ice cream were produced by using different proportions of sucrose and Stevia. Physicochemical characteristics, hedonic sensory evaluations and glycemic index determination of products were carried out by following conventional methods. Replacement of sucrose with Stevia resulted in a significantly lower viscosity and brix with a higher overrun and melting rate in a dose dependent manner. Total replacing of sucrose with Stevia resulted in significant reduction in caloric value from 143.03 to 105.25 Kcal and GI from 79.06 ± 4.0 to 72.18 ± 5.27 as compared to those of sucrose based formulation (p 0.05) indicating a 37.78% and 6.88% reduction, respectively. TB had the best sensory acceptance among all the treatments. We concluded that substitution of sucrose with Stevia may be a choice to produce low caloric and GI ice creams. However, using mixture of the two sweeteners improves sensory acceptance of the formulations.
