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 choice of extrusion process is a decisive factor that affects the finished product quality for polybag manufacturing. One important component influencing the quality of the finished product is the selection of the...The choice of extrusion process is a decisive factor that affects the finished product quality for polybag manufacturing. One important component influencing the quality of the finished product is the selection of the extrusion technique. Two popular procedures that vary in the kind of dye used and the final product’s texture are cast film and blown film. In the horizontal extrusion moulding method known as “cast film”, heated resin is injected into a flat dye and allowed to cool on chill rolls. The film produced is clear, lightweight, and appropriate for lamination;its thickness varies based on the winding speed and the film is slower to crystallize and has less clarity but more durability because the resin molecules have reoriented, facing limitation of high wastage generation. This study primarily focused on the preparation of polybag film using the blown film extrusion process, utilizing high-quality polymer resins such as polyester polyethylene (PP) and linear low-density polyethylene (LLDPE) to minimize waste generation. The novelty of the process was reflected in minimising the waste generation. The control parameters considered in this study are temperature, pressure, and air intake volume. We investigated the influence of these critical process control parameters on the gauge thickness, optical properties, and mechanical strength of the polybag film produced through blown film extrusion. Additionally, we replicated the blown film process using simulation software developed at Pennsylvania College of Technology. The simulation results confirmed the overall stability of the polybag film produced through the blown film extrusion process.展开更多
热塑性聚乙烯基电缆绝缘材料具有优于交联聚乙烯(cross-linked polyethylene,XLPE)的电气和机械性能,有望成为新一代绿色环保的电缆绝缘材料。在电缆结构设计中,保守的安全绝缘厚度使得电缆的生产成本增加,降低绝缘层的击穿电场强度;并...热塑性聚乙烯基电缆绝缘材料具有优于交联聚乙烯(cross-linked polyethylene,XLPE)的电气和机械性能,有望成为新一代绿色环保的电缆绝缘材料。在电缆结构设计中,保守的安全绝缘厚度使得电缆的生产成本增加,降低绝缘层的击穿电场强度;并且在电缆实际运行过程中,绝缘材料往往工作在70~90℃高温环境下;因此针对新型绝缘材料,温度及厚度对其击穿电场强度的影响研究具有工程实际意义。以线性低密度聚乙烯(linear low density polyethylene,LLDPE)/高密度聚乙烯(high density polyethylene,HDPE)共混绝缘材料为研究对象,进行不同温度下(30、70、90、105℃)及不同厚度下的工频击穿实验,研究温度和厚度对其交流击穿的影响。测试结果表明:相较于XLPE绝缘材料,70L-30H(即LLDPE与HDPE在配比为7∶3的情况下熔融共混得到的绝缘材料)具有较高的工频击穿电场强度,在低于工况温度环境下,其击穿电场强度的温度稳定性较高;然而70L-30H的工频击穿电场强度受厚度影响程度略高,但在相同厚度下其击穿电场强度仍明显高于XLPE。上述研究可为热塑性聚乙烯基电缆绝缘材料研发提供参考。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 choice of extrusion process is a decisive factor that affects the finished product quality for polybag manufacturing. One important component influencing the quality of the finished product is the selection of the extrusion technique. Two popular procedures that vary in the kind of dye used and the final product’s texture are cast film and blown film. In the horizontal extrusion moulding method known as “cast film”, heated resin is injected into a flat dye and allowed to cool on chill rolls. The film produced is clear, lightweight, and appropriate for lamination;its thickness varies based on the winding speed and the film is slower to crystallize and has less clarity but more durability because the resin molecules have reoriented, facing limitation of high wastage generation. This study primarily focused on the preparation of polybag film using the blown film extrusion process, utilizing high-quality polymer resins such as polyester polyethylene (PP) and linear low-density polyethylene (LLDPE) to minimize waste generation. The novelty of the process was reflected in minimising the waste generation. The control parameters considered in this study are temperature, pressure, and air intake volume. We investigated the influence of these critical process control parameters on the gauge thickness, optical properties, and mechanical strength of the polybag film produced through blown film extrusion. Additionally, we replicated the blown film process using simulation software developed at Pennsylvania College of Technology. The simulation results confirmed the overall stability of the polybag film produced through the blown film extrusion process.
文摘热塑性聚乙烯基电缆绝缘材料具有优于交联聚乙烯(cross-linked polyethylene,XLPE)的电气和机械性能,有望成为新一代绿色环保的电缆绝缘材料。在电缆结构设计中,保守的安全绝缘厚度使得电缆的生产成本增加,降低绝缘层的击穿电场强度;并且在电缆实际运行过程中,绝缘材料往往工作在70~90℃高温环境下;因此针对新型绝缘材料,温度及厚度对其击穿电场强度的影响研究具有工程实际意义。以线性低密度聚乙烯(linear low density polyethylene,LLDPE)/高密度聚乙烯(high density polyethylene,HDPE)共混绝缘材料为研究对象,进行不同温度下(30、70、90、105℃)及不同厚度下的工频击穿实验,研究温度和厚度对其交流击穿的影响。测试结果表明:相较于XLPE绝缘材料,70L-30H(即LLDPE与HDPE在配比为7∶3的情况下熔融共混得到的绝缘材料)具有较高的工频击穿电场强度,在低于工况温度环境下,其击穿电场强度的温度稳定性较高;然而70L-30H的工频击穿电场强度受厚度影响程度略高,但在相同厚度下其击穿电场强度仍明显高于XLPE。上述研究可为热塑性聚乙烯基电缆绝缘材料研发提供参考。
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.
