Operational Performance Evaluation of Corn Processing Industry Technological Innovation Alliance Based on Survey Data in Heilongjiang Province

This paper selected the corn processing industry technology innovation alliance in Heilongjiang Province as the research object, evaluated the operational performance of the alliance by using analytic hierarchy process(AHP) and fuzzy comprehensive evaluation methods. AHP empirical results showed that the satisfaction of information communication and the satisfaction of the management process were the weakest. And the order from high to low on the level of indicators of the impact for the alliance was the result of alliance operations and the process of alliance operations, the behavioral attitude of alliance members. Besides, the results of fuzzy comprehensive evaluation showed that the operational performance of the corn processing industry technology innovation alliance in Heilongjiang Province was in the general level.

Development Situation and Strategy Analysis of Waxy Corn Processing Industry in Chongqing Municipality

With the development of society and economy and increasing awareness of people's diet and health care,the demand for waxy corn and its processed products has been rising. At present,the planting of waxy corn in Chongqing is taking shape,but the waxy corn processing is still in the initial stage with smaller enterprise scale and fewer processing product variety. Based on the analysis of the development advantages and disadvantages of waxy corn processing industry in Chongqing,this paper brings forward the development ideas and strategies of Chongqing waxy corn processing industry from three aspects of production,processing and policy.

Factor and Cluster Analysis on the Competitiveness of Agri-food Processing Industry in China

This paper establishes 13 evaluation indicators for the competitiveness of agri-food processing industry,uses factor analysis to evaluate the competitiveness of agri-food processing industry in 31 provinces(cities,autonomous regions)of China,and does cluster analysis to divide these regions into several categories according to the difference in competitiveness,in order to understand the level of competitiveness of agri-food processing industry in China.

Analysis on Development Characteristics of Agricultural Product Processing Industry under the New Normal

From the beginning of the 21^(st)century to 2013,the economic income of main business of agricultural products processing enterprises in China had maintained above double digits for a long time.The current traditional high-speed growth will be transformed to high-quality,mediumhigh-speed development,and the development trend is in line with economic laws and macro situation characteristics.With the acceleration of spa-tial distribution and cluster development of agricultural processing industry,the late-mover advantages in the central and western regions of China are gradually emerging.With the support of Internet+and e-commerce online shopping platforms,the integrative development with related industries has been deepened.Led by the new concept of green development,the demand of processing industry of green,healthy,specific functional food(such as diabetes,hypertension and other specific groups)is booming.In the aspect of development strategy,it is appropriate to build multivariate information service platform,improve the technical cooperation platform,and provide software and hardware facilities for further development of agricultural product processing industry.Combined with local economic development advantages,resource advantages and industrial advantages and other factors,the way of differentiation,regionalization and characterization should be taken according to local conditions and following the law,so as to energize the rural revitalization.

Study on Innovation System and Innovation Strategy of Chinese Agricultural Product Processing Industry

The agricultural product processing industry is the inevitable choice for the agriculture to realize the industrialization, integration and modernization. Although the agricultural product processing industry has become the bright point in Chinese economy development, the whole development level falls behind the developed countries. The thesis brings up that the inherent reasons that Chinese agricultural product processing industry falls behind is that Chinese agricultural product processing industry has not an integrated industrial innovation system and has not a proper innovation strategy. So this thesis deeply discusses how to construct innovation system of Chinese agricultural product processing industry and puts forward the innovation strategy in order to improve the technology innovation capability and the development level.

China's Aluminum Processing Industry Showed Differentiation;High-end Aluminum Becomes a Breakthrough

China’s aluminum processing industry showed differentiation,resulting in the unbalanced allocation of profits in domestic aluminum processing enterprises in 2016.Among them,the medium and low-end deep processing enterprises saw a profit decline,while high-end enterprises were the other case.Due to a lack of R&D investment in

Effective technology for processing industrial volatile organic compounds by the atmospheric pressure microwave plasma torch

In this study,we investigated the abatement of volatile organic compounds(VOCs)by the atmospheric pressure microwave plasma torch(AMPT).To study the treatment efficiency of AMPT,we used the toluene and water-based varnish to simulate VOCs,respectively.By measuring the compounds and contents of the mixture gas before/after the microwave plasma process,we have calculated the treatment efficiency of AMPT.The experimental results show that the treatment efficiency of AMPT for toluene with a concentration of 17.32×10^(4) ppm is up to 60 g/kWh with the removal rate of 86%.For the volatile compounds of water-based varnish,the removal efficiency is up to 97.99%.We have demonstrated the higher potential for VOCs removal of the AMPT process.

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

An Analysis of Development Trend of Food Industry in Three Northeastern Provinces of China

The food industry has developed into a leading industry in Northeast China.However,various economic indicators have shown a significant downward trend in recent years.Through the systematic analysis of the development trend of food industry in Northeast China in the past ten years,especially in 2014,it is found that in the context of stable and smooth operation of the food industry in China,the efficiency of the main processing industries declines in the three northeastern provinces of China,there is a simultaneous decline in the share of output value of the leading industries in the output value of manufacturing sector in three provinces,the collective and individual capital withdrawal is obvious in the paid-up capital,and there is a simultaneous decline in the industrial production and sales rate and the export delivery value.The food industry in Northeast China has plunged into the dilemma of development.Finally,this article puts forward some targeted recommendations from aspects of policy making,industrial agglomeration and enterprise cultivation.

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Rice Drying,Storage and Processing:Effects of Post-Harvest Operations on Grain Quality

Various post-harvest processes of rice are commonly employed,especially during the off-season,to ensure its consumption feasibility,which often affect the grain quality.Different forms of drying,storage and processing of rice are evaluated to identify their effects on grain quality.Microwave drying has emerged as an alternative to the widely-used intermittent-drying and fixed-bed-dryer methods of drying paddy rice.Control of drying-air temperatures(between 40℃ and 60℃)according to the rice variety can improve quality,especially for exotic varieties.Keeping stored grain in hygroscopic balance,with water content between 11%to 15%,at temperatures between 16℃ and 20℃ and with intergranular relative humidity near 60%,allows 12 months of storage in a controlled environment without significant deterioration.Other innovations,notably the application of artificial refrigeration to grain stored in bulk in vertical cylindrical silos and the use of impermeable packaging for storage,ensure the conservation of grain mass.The different stages and equipments used to obtain polished,brown and parboiled rice result in significant changes in the nutritional value of rice because of the removal of the outermost layers of the grains.Polishing reduces the nutritional value and physical homogeneity of rice.Brown rice retains more bioactive compounds and nutrients because it does not lose the outer layer of the grains in the polishing processes.Parboiled rice,although less nutritious than brown rice,has better grain integrity and milling yield and less loss of nutrients than white rice.

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

Industrial Recycling Process of Batteries for EVs

The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases(DRDB)in recent years,it is widely used in business and transactional applications.In long-term use,some problems of disk databases are gradually exposed.For applications with high real-time requirements,the performance of using disk database is not satisfactory.In the context of the booming development of the Internet of things,domestic real-time databases have also gradually developed.Still,most of them only support the storage,processing,and analysis of data values with fewer data types,which can not fully meet the current industrial process control system data types,complex sources,fast update speed,and other needs.Facing the business needs of efficient data collection and storage of the Internet of things,this paper optimizes the transaction processing efficiency and data storage performance of the memory database,constructs a lightweight real-time memory database transaction processing and data storage model,realizes a lightweight real-time memory database transaction processing and data storage model,and improves the reliability and efficiency of the database.Through simulation,we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU(Least Recently Used)algorithm.Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

Evaluation and simulation analysis of China's copper security evolution trajectory

Copper metal is one important raw material of national economy, and its security is tightly linked with industrialization process. A comprehensive security evaluation index system, including three first class indicators, nine second class indicators and fourteen third class indicators, was constructed based on the pressure-state-response （PSR） model. And efficacy coeff^cient method was adopted in dimensionless treatment and entropy method was used to obtain the weight. Then, the data of China＇s copper from 1992 to 2011 were studied and collected. By processing these data, a comprehensive safety index of China＇s copper was got. The results showed that the comprehensive safety index of China＇s copper rose from its low point. The safety pressure state of China＇s copper was improved. The safety state of China＇s copper continued to be tight. The response state continued to rise. The overall situation of China＇s copper safety was improving, but still not optimistic.

Magnetic separation studies on ferruginous chromite fine to enhance Cr:Fe ratio

The Cr：Fe ratio （chromium-to-iron mass ratio） of chromite affects the production of chrome-based ferroalloys. Although the lit- erature contains numerous reports related to the magnetic separation of different minerals, limited work concerning the application of mag- netic separation to fine chromite from the Sukinda region of India to enhance its Cr：Fe ratio has been reported. In the present investigation, magnetic separation and mineralogical characterization studies of chromite fines were conducted to enhance the Cr：Fe ratio. Characterization studies included particle size and chemical analyses, X-ray diffraction analysis, automated mineral analysis, sink-and-float studies, and mag- netic susceptibility measurements, whereas magnetic separation was investigated using a rare earth drum magnetic separator, a rare earth roll magnetic separator, an induced roll magnetic separator, and a wet high-intensity magnetic separator. The fine chromite was observed to be upgraded to a Cr：Fe ratio of 2.2 with a yield of 55.7% through the use of an induced roll magnetic separator and a feed material with a Cr：Fe ratio of 1.6.

Importance, features and uses of metal oxide catalysts in heterogeneous catalysis

This short review paper aims at assembling the present state of the art of the multiuses of metal oxides in heterogeneous catalysis, concerning liquid and gaseous phases of the reactant mixtures on solid catalysts. It includes the description of the main types of metal oxide catalysts, of their various preparation procedures and of the main reactions catalysed by them (acid-base type, selective and total oxidations, bi-functional catalysis, photocatalysis, biomass treatments, environmental catalysis and some of the numerous industrial applications). Challenges and prospectives are also discussed.

Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information,a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy,