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,

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Fault degradation prognostic, which estimates the time before a failure occurs and process breakdowns, has been recognized as a key component in maintenance strategies nowadays. Fault degradation processes are, in general,slowly varying and can be modeled by autoregressive models. However, industrial processes always show typical nonstationary nature, which may bring two challenges: how to capture fault degradation information and how to model nonstationary processes. To address the critical issues, a novel fault degradation modeling and online fault prognostic strategy is developed in this paper. First, a fault degradation-oriented slow feature analysis(FDSFA) algorithm is proposed to extract fault degradation directions along which candidate fault degradation features are extracted. The trend ability assessment is then applied to select major fault degradation features. Second, a key fault degradation factor(KFDF) is calculated to characterize the fault degradation tendency by combining major fault degradation features and their stability weighting factors. After that, a time-varying regression model with temporal smoothness regularization is established considering nonstationary characteristics. On the basis of updating strategy, an online fault prognostic model is further developed by analyzing and modeling the prediction errors. The performance of the proposed method is illustrated with a real industrial process.

Scenario analysis on abating industrial process greenhouse gas emissions from adipic acid production in China

Adipic acid is an important petrochemical product,and its production process emits a high concentration of greenhouse gas N_2 O.This paper aims to provide quantitative references for relevant authorities to formulate greenhouse gas control roadmaps.The forecasting method of this paper is consistent with the published national inventory in terms of caliber.Based on the N_2 O abatement technical parameters of adipic acid and the production trend,this paper combines the scenario analysis and provides a measurement of comprehensive N_2 O abatement effect of the entire industry in China.Four future scenarios are assumed.The baseline scenario(BAUS) is a frozen scenario.Three emission abatement scenarios(ANAS,SNAS,and ENAS) are assumed under different strength of abatement driving parameters.The results show that China's adipic acid production process can achieve increasingly significant N_2 O emission abatement effects.Compared to the baseline scenario,by 2030,the N_2 O emission abatements of the three emission abatement scenarios can reach 207-399 kt and the emission abatement ratios can reach 32.5%-62.6%.By 2050,the N_2 O emission abatements for the three emission abatement scenarios can reach 387-540 kt and the emission abatement ratios can reach 71.4%-99.6%.

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.

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.

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.

Investigation into the Methodology and Implementation of Life Cycle Engineering under China’s Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China.In pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing industries.An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology.This strategy aims to attain an optimal solution for material performance,resource consumption,and environmental impact.In this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and reutilization.The meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing process.This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an example.The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

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.

Comparative analysis of greenhouse gas emission inventory for Pakistan:Part I energy and industrial processes and product use

In order to further improve the accuracy and reliability and reduce uncertainties in the national GHG inventories for Pakistan,this study call for using 2006 IPCC Guidelines,to help to identify the national targets for GHG mitigation with respect to the nationally determined contributions(NDCs).GHG(CO2,CH4,and N20)inventories for Pakistan have been developed by conducting a detailed sectoral assessment of IPCC source sectors,energy,industrial processes and product use(IPPU),agriculture,forestry and other land use(AFOLU),and the waste sector.Further,sector wise comparative analysis of GHG inventories(1994-2017)based on the 2006 and 1996 IPCC Guidelines have also been presented.Results indicated an average relative difference of 4%in total GHG emissions(CO2 equivalent)from energy sector between 2006 and 1996 IPCC Guidelines.With 3.6%average annual growth rate based on 2006 IPCC Guidelines,CO2 from energy sector remained the most abundant GHG emitted,followed by CH4 and N2O.While the average absolute difference in emissions of CH4 and N20 from the energy sector is notable,the total estimated GHG emissions by 2006 IPCC Guidelines duplicate those by 1996 IPCC Guidelines.In the mineral industry with 2006 IPCC Guidelines,an average annual growth rate of 6.7%is observed,contributing 64%of total IPPU sector CO2 emissions.Nevertheless,the relative difference between the two Guidelines in overall IPPU sector emissions remained negligible.There might be a need for switching to 2006 IPCC Guidelines to consider more parameters such as additional source sectors and new default emission factors that fit into national circumstances.

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.

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.

Optimization of Distributed Integrated Multi-energy System Considering Industrial Process Based on Energy Hub

As a typical scenario of distributed integrated multi-energy system(DIMS),industrial park contains complex production constraints and strong associations between industrial productions and energy demands.The industrial production process(IPP)consists of controllable subtasks and strict timing constraints.Taking IPP as a control variable of optimal scheduling,it is an available approach that models the IPP as material flow into an extension energy hub(EH)to achieve the optimization of industrial park.In this paper,considering the coupling between the production process and energy demands,a model of IPP is proposed by dividing the process into different adjustable steps,including continuous subtask,discrete subtask,and storage subtask.Then,a transport model of material flow is used to describe the IPP in an industrial park DIMS.Based on the concept of EH,a universal extension EH model is proposed considering the coupling among electricity,heat,cooling,and material.Furthermore,an optimal scheduling method for industrial park DIMS is proposed to improve the energy efficiency and operation economy.Finally,a case study of a typical battery factory is shown to illustrate the proposed method.The simulation results demonstrate that such a method reduces the operation cost and accurately reflects the operation state of the industrial factory.

On Numerical Modelling of Industrial Powder Compaction Processes for Large Deformation of Endochronic Plasticity at Finite Strains

Compaction processes are one the most important par ts of powder forming technology. The main applications are focused on pieces for a utomotive, aeronautic, electric and electronic industries. The main goals of the compaction processes are to obtain a compact with the geometrical requirements, without cracks, and with a uniform distribution of density. Design of such proc esses consist, essentially, in determine the sequence and relative displacements of die and punches in order to achieve such goals. A.B. Khoei presented a gener al framework for the finite element simulation of powder forming processes based on the following aspects; a large displacement formulation, centred on a total and updated Lagrangian formulation; an adaptive finite element strategy based on error estimates and automatic remeshing techniques; a cap model based on a hard ening rule in modelling of the highly non-linear behaviour of material; and the use of an efficient contact algorithm in the context of an interface element fo rmulation. In these references, the non-linear behaviour of powder was adequately desc ribed by the cap plasticity model. However, it suffers from a serious deficiency when the stress-point reaches a yield surface. In the flow theory of plasticit y, the transition from an elastic state to an elasto-plastic state appears more or less abruptly. For powder material it is very difficult to define the locati on of yield surface, because there is no distinct transition from elastic to ela stic-plastic behaviour. Results of experimental test on some hard met al powder show that the plastic effects were begun immediately upon loading. In such mater ials the domain of the yield surface would collapse to a point, so making the di rection of plastic increment indeterminate, because all directions are normal to a point. Thus, the classical plasticity theory cannot deal with such materials and an advanced constitutive theory is necessary. In the present paper, the constitutive equations of powder materials will be discussed via an endochronic theory of plasticity. This theory provides a unifi ed point of view to describe the elastic-plastic behaviour of material since it places no requirement for a yield surface and a ’loading function’ to disting uish between loading an unloading. Endochronic theory of plasticity has been app lied to a number of metallic materials, concrete and sand, but to the knowledge of authors, no numerical scheme of the model has been applied to powder material . In the present paper, a new approach is developed based on an endochronic rate independent, density-dependent plasticity model for describing the isothermal deformation behavior of metal powder at low homologous temperature. Although the concept of yield surface has not been explicitly assumed in endochronic theory, it is shown that the cone-cap plasticity yield surface (Fig.1), which is the m ost commonly used plasticity models for describing the behavior of powder materi al can be easily derived as a special case of the proposed endochronic theory. Fig.1 Trace of cone-cap yield function on the meridian pl ane for different relative density As large deformation is observed in powder compaction process, a hypoelastic-pl astic formulation is developed in the context of finite deformation plasticity. Constitutive equations are stated in unrotated frame of reference that greatly s implifies endochronic constitutive relation in finite plasticity. Constitutive e quations of the endochronic theory and their numerical integration are establish ed and procedures for determining material parameters of the model are demonstra ted. Finally, the numerical schemes are examined for efficiency in the model ling of a tip shaped component, as shown in Fig.2. Fig.2 A shaped tip component. a) Geometry, boundary conditio n and finite element mesh; b) density distribution at final stage of

Development of the Mechanical System on a Third-Generation Industrial Computed Tomography Scanner in Brazil

The development of measurement geometry for medical X-ray computed tomography （CT） scanners was carried out from the first to the fourth-generation. This concept has also been applied for imaging of industrial processes such as pipe flows or for improving design, operation, optimization and troubleshooting. Nowadays, gamma CT permits to visualize failure equipment points in three-dimensional analysis and in sections of chemical and petrochemical industries. The aim of this work is the development of the mechanical system on a third-generation industrial CT scanner to analyze laboratorial process columns which perform highly efficient separation, turning the ^6oCo, ^75Se, ^137Cs and/or ^192Ir sealed gamma-ray source（s） and the NaI（Tl） multidetector array. It also has a translation movement along the column axis to obtain as many slices of the process flow as needed. The mechanical assembly for this third-generation industrial CT scanner is comprised by strength and rigidity structural frame in stainless and carbon steels, rotating table, source shield and collimator with pneumatic exposure system, spur gear system, translator, rotary stage, drives and stepper motors. The use of suitable spur gears has given a good repeatability and high accuracy in the degree of veracity. The data acquisition boards, mechanical control interfaces, software for movement control and image reconstruction were specially development. A multiphase phantom capable to be setting with solid, liquid and gas was testing. The scanner was setting for 90 views and 19 projections for each detector totalizing 11,970 projections. Experiments to determine the linear attenuation coefficients of the phantom were carried out which applied the Lambert-Beer principle. Results showed that it was possible to distinguish between the phases even the polymethylmethacrylate and the water have very similar density and linear attenuation coefficients. It was established that the newly developed third-generation fan-beam arrangement gamma scanner unit has a good spatial resolution acceptable given the size of the used phantom in this study. The tomografic reconstruction algorithm in used 60 ~ 60 pixels images was the Alternative Minimization （AM） technique and was implemented in MATLAB and VB platforms. The mechanical system presented a good performance in terms of strength, rigidity, accuracy and repeatability with great potential to be used for education or program which dedicated to training chemical and petrochemical industry professionals and for industrial process optimization in Brazil.

Research on Application of Ultrafiltration Technology in Chemical Process

Ultrafiltration is a new practical technique of a chemical process, its development prospect is very broad, so it is a very wide application in chemical process, this paper combined with ultrafiltration technique in a ultrafiltration company, the ultrafiltration technique should be used to analyzes and discusses in ultrafiltration process. Finally, the article gives the process of ultrafiltration technology in city living water, ultrafiltration technology has the advantages of simple process, convenient operation, low energy consumption, good removal effect of phosphorus in chemical enhanced ultrafiltration micelle research field.

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.

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.

Generic reconstruction technology based on RST for multivariate time series of complex process industries

In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.

An Investment Decision Support System for Process Industries

Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process industries should be executed under three strategic objectives--enterprise benefits, social benefits and customer benefits. A systematic investment evaluation and decision-making method with a four-step procedure based on the analytic hierarchy process (AHP) is proposed to evaluate various qualitative and quantitative elements with various criteria. At the first step, the decision hierarchy is constructed under the three strategic objectives. Second, pair-wise comparison is utilized to evaluate the weights of elements and criteria. Third, qualitative elements are quantified by pair-wise comparison and quantitative elements are re-scaled by a uniform criterion. At the last, the best choice is made through synthesizing values upward in the hierarchy. An investment decision support system (DSS) is developed based on Microsoft Excel, and applied to a retrofit investment of united fluid catalytic cracking(FCC) and liquefied gas separation process in a refinery plant.

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.