Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Operation optimization of the steel manufacturing process: A brief review

Against the realistic background of excess production capacity, product structure imbalance, and high material and energy consumption in steel enterprises, the implementation of operation optimization for the steel manufacturing process is essential to reduce the production cost, increase the production or energy efficiency, and improve production management. In this study, the operation optimization problem of the steel manufacturing process, which needed to go through a complex production organization from customers' orders to workshop production, was analyzed. The existing research on the operation optimization techniques, including process simulation, production planning, production scheduling, interface scheduling, and scheduling of auxiliary equipment, was reviewed. The literature review reveals that, although considerable research has been conducted to optimize the operation of steel production, these techniques are usually independent and unsystematic.Therefore, the future work related to operation optimization of the steel manufacturing process based on the integration of multi technologies and the intersection of multi disciplines were summarized.

Application of Functional Operators with Shift to the Study of Renewable Systems When the Reproductive Process Is Described by Integrals with Degenerate Kernels

We continue studying systems whose state depends on time and whose resources are renewably based on functional operators with shift. In previous articles, we considered the term which described results of reproductive processes as a linear expression or as a shift summand. In this work, the reproductive term is represented using an integral with a degenerate kernel. A cyclic model of evolution of the system with a renewable resource is developed. We propose a method for solving the balance equation and we determine an equilibrium state of the system. Having applied this model, we can investigate problems of natural systems and their resource production.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

A Novel Close-loop Strategy for Integrating Process Operations of Fluidized Catalytic Cracking Unit with Production Planning Optimization

普通当模特儿的系统产生的生产计划模型不为进程操作包含限制，并且一个解决方案由这些模型优化被称为一个伪最佳的计划。伪最佳的计划不能为没有相应操作条件某时在实践被执行。以便决定一个实际上可行的最佳的计划和联合起来的使流体化的催化裂开的相应操作条件(FCCU ) ，新奇靠近环的综合策略包括一个伪最佳的计划的决心，操作 FCCU 的条件和计划模型的生产的修订寻找，在这篇文章被建议。在策略，一个概括基因算法(GA ) 结合了 FCCU 的一个顺序的过程模拟器被使用寻找实现 FCCU 的伪最佳的计划的操作条件并且作为一个最后的基因个人在 GA 搜索输出最佳的个人。当没有相应操作条件被发现时，最后的基因个人基于修正(FGIC ) 方法被介绍修订计划模型的生产，然后一个新伪最佳的生产计划是坚定的。上述步被重复直到一个实际上可行的最佳的计划和 FCCU 的相应操作条件被获得。靠近环的综合策略被二个盒子验证，并且策略在决定一个实际上执行的最佳的计划和 FCCU 的相应操作条件是有效的，这被显示。

EFFECT OF BURNING METHOD ON THE FORMING PROCESSES OF PORTLAND CEMENT CLINKER

The forming temperature of Clinker melt underdifferent burning conditions has been studied by appearance examination and thermal shrinker determination, and the viscosity of melt has been discussed by studying the coordination number of Al^(3+) and Fe^(3+) in cement clinker burned by different method with x^- ray fluorescence analysis and Moss- bauer spectroscopy. The results show that the clin- ker melt under rapid burning may come into exis- tence at lower temperature and It's viscosity is lower. So the forming processes of clinker may be different at rapid burning from ordinary burning. They are probably an important factor to promote the formation of clinker burned at lower temperature with rapid burning method.

Controlling of crustacean zooplankton reproduction in biological activated carbon (BAC) filters by strengthen operation and management of conventional process

To counter the mass reproduction and penetration of crustacean zooplankton in Biological Activated Carbon(BAC)filters which may result in the presence of organisms in potable water and water pollution,this paper analyzed the factors affecting organisms' reproduction in BAC filters.A comparative study was performed on the density and composition of crustacean zooplankton of the concerned water treatment units of two advanced water plants(Plant A and B)which with the same raw water and the same treatment technique in southern China.The results obtained show that the crustaceans' density and composition was very different between the sand filtered water of Plant A and Plant B.which Harpacticoida bred sharply in the sediment tanks and penetrated sand filter into BAC filters was the primary reason of crustaceans reproduce in BAC filters of Plant A.For prevention of the organisms reproduction in BAC,some strengthen measures was taken including pre-chlorination,cleaning coagulation tanks and sediment tanks completely,increasing sludge disposal frequency to stop organisms enter BAC filters,and the finished water quality was improved and enhanced.

Some Results about the Sample Path Properties of Markov Processes with Independent Self-Similar Components

This paper considers a special class of operator self-similar processes Markov processes {X（t）, t≥0} with independent self-similar components, that is, X （ t ） =（X^1（t）,…,X^d（t））, where {X^i（t）,t≥0}, i=1,2,…,d are d independent real valued self-similar Markov processes. By means of Brel-Cantelli lemma, we give two results about asymptotic property as t→∞ of sample paths for two special classes of Markov processes with independent self-similar components.

FTC of hidden Markov process with application to resource allocation in air operation

This paper investigates the feedback control of hidden Markov process（HMP） in the face of loss of some observation processes.The control action facilitates or impedes some particular transitions from an inferred current state in the attempt to maximize the probability that the HMP is driven to a desirable absorbing state.This control problem is motivated by the need for judicious resource allocation to win an air operation involving two opposing forces.The effectiveness of a receding horizon control scheme based on the inferred discrete state is examined.Tolerance to loss of sensors that help determine the state of the air operation is achieved through a decentralized scheme that estimates a continuous state from measurements of linear models with additive noise.The discrete state of the HMP is identified using three well-known detection schemes.The sub-optimal control policy based on the detected state is implemented on-line in a closed-loop,where the air operation is simulated as a stochastic process with SimEvents,and the measurement process is simulated for a range of single sensor loss rates.

Study on Winter Operation Process of the Surface Flow Constructed Wetland in Tianjin Area

[Objective] The research aimed to study winter operation process of the surface flow constructed wetland in ＂rianjin area. [Method] In view of climate characteristics in Tianjin, by the way of running under the ice, winter operation experiment of the surface flow constructed wetland in Tianjin was conducted, with the expectation to get some useful process parameters to run such systems in North China in winter. [ Result] Although purification effect of the sewage by surface flow constructed wetland in winter was worse than that in other seasons （ average reduction of about 20%）, surface flow constructed wetland running under the ice was feasible in Tianjin area. When surface flow constructed wetland in North China ran under ice in winter, it was suggested that the outlet must be located in a low position to prevent to be completely frozen, and running water depth should not be less than 50 -60 cm. The hydraulic load could be raised on the basis of reflux, and hydraulic retention time should maintain less than 4 d to keep water-soil interface not freezing. Inlet water depth should be increased as much as possible to improve temperature in the system. V Conclusion1 The research could provide reference for promotion and application of the surface flow constructed wetland in North China.

STOCHASTIC MODEL OF PERIODIC OPERATION PERFORMANCE FOR THE CONTINUOUS COUNTER－CURRENT ADSORPTION PROCESS

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Integral Operator Solving Process of the Boundary Value Problem of Abstract Kinetic Equation with the First Kind of Critical Parameter and Generalized Periodic Boundary Conditions

在这份报纸，边界的概念与第一种批评参数珍视抽象运动方程的问题 0 并且概括周期的边界条件在由函数组成，向量在一个一般 Banach 空格珍视的一个 Lebesgue 空格被介绍，然后描述这些的解决方案由 Volterra 的抽象线性不可分的操作符的抽象边界值问题类型。我们把这个过程称为解决过程的不可分的操作员。

Autonomous process collaboration framework for C4ISR system interoperation

Interoperability plays an important role in the joint command, control, communication, computer, intelligence, surveillance, reconnaissance(C4 ISR) operations. Coordinating and integrating operational processes to fulfill a common mission are challenged by the ever-changing battlefield and hence requires a cross-organizational process management that produces an autonomous, flexible and adaptable architecture for collaborative process evolution. The traditional business process collaboration pattern is based on the predefined "public-view" perspective and cannot meet the requirement of the joint task operations. This paper proposes a flexible visibility control mechanism and a dynamic collaboration framework for modeling and generating collaborative processes. The mechanism allows collaborators to define a set of visibility rules to generate different views of the private processes for different collaborations, which gives a great flexibility for the collaboration initiator to decide on an appropriate collaboration pattern. The framework supports collaborators to dynamically and recursively add a new process or even a new organization to an existing collaboration. Moreover, a formal representation of the processes and a set of generation algorithms are provided to consolidate the proposed theory.

Review on the study of metallurgical process engineering

After nearly one hundred years of research, metallurgy(metallurgical science and engineering) has gradually become a system with three levels of knowledge:(1) micro metallurgy at the atomic/molecular scale,(2) process metallurgy at the procedure/device, and(3) macrodynamic metallurgy at the full process/process group. Macro-dynamic metallurgy development must eliminate the concept of an "isolated system" and establish concepts of "flow," "process network," and "operating program" to study the "structure–function–efficiency" in the macrodynamic operation of metallurgical manufacturing processes. It means considering "flow" as the ontology and observing dynamic change by"flow" to solve the green and intelligent potential of metallurgical enterprises. Metallurgical process engineering is integrated metallurgy, toplevel designed metallurgy, macro-dynamic operated metallurgy, and engineering science level metallurgy. Metallurgical process engineering is a cross-level, comprehensive, and integrated study of the macro-dynamic operation of manufacturing processes. Metallurgical process engineering studies the physical nature and constitutive characteristics of the dynamic operation of steel manufacturing process, as well as the analysis-optimization of the set of procedure functions, coordination-optimization of the set of procedures' relations, and reconstruction-optimization of the set of procedures in the manufacturing process. The study establishes rules for the macro operation of the manufacturing process, as well as dynamic and precise objectives of engineering design and production operation.

Identification Scheme for Fractional Hammerstein Models With the Delayed Haar Wavelet

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix(HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.

Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections

The quantitative evaluation of multi-process collaborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–continuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to conduct the evaluation, due to the lack of full-scale consideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess collaborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster coordinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process collaborative operation level was improved with fewer adjustments and interruptions in casting.

A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process

Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.

Preparation of optimal entropy squeezing state of atomic qubit inside the cavity via two-photon process and manipulation of atomic qubit outside the cavity

Considering two atomic qubits initially in Bell states, we send one qubit into a vacuum cavity with two-photon resonance and leave the other one outside. Using quantum information entropy squeezing theory, the time evolutions of the entropy squeezing factor of the atomic qubit inside the cavity are discussed for two cases, i.e., before and after rotation and measurement of the atomic qubit outside the cavity. It is shown that the atomic qubit inside the cavity has no entropy squeezing phenomenon and is always in a decoherent state before the operating atomic qubit outside the cavity. However,the periodical entropy squeezing phenomenon emerges and the optimal entropy squeezing state can be prepared for the atomic qubit inside the cavity by adjusting the rotation angle, choosing the interaction time between the atomic qubit and the cavity, controlling the probability amplitudes of subsystem states. Its physical essence is cutting the entanglement between the atomic qubit and its environment, causing the atomic qubit inside the cavity to change from the initial decoherent state into maximum coherent superposition state, which is a possible way of recovering the coherence of a single atomic qubit in the noise environment.

Designing Software to Analyze Sewing Process of Industrial Knitted Products

In the textile industry,garment manufacturing contains four major pro-cesses containing cutting,sewing,finishing,and packaging.Sewing is the most crucial and intricate section,dealing with a large number of varied operations.A successful sewing process needs to be optimized regarding different factors,including time,sewing equipment,and skilled workers.Assembly lineflow is combined by a set of operations with a particular sequence.The utmost impor-tance of all garment industry is to arrange the workstations to minimize the num-ber of employees in order to produce at the best productive rate with the most reasonable cost,shortest time,and satisfying quality.In most garment factories,the production lines are balanced using the empirical judgment of the line man-agers.For the whole process the data of production time at each step,labor pro-ductivity,proper choices of equipment were always needed to calculate line efficiency.As far as the issue is concerned,there has not been an academically sewing process analyzing software providing adequate data of sewing motions and sewing time as the credible input for the line balancing tasks.Towards this goal,this paper presents the results of research on optimizing academically self-built software to analyze the sewing process of knitted products applied to industrial production using Java programming language on Google tools.The results achieved by the software are not only to analyze sewing products and the technological sewing process,calculate the sewing time on the machine but also analyze the sewing activities of workers into manipulations,movements,and motions to calculate the preparation time for two typical knitted products,namely,Polo-Shirt and T-Shirt with the case studies at General Textile Garment Joint Stock Company Hanoi and Star Fashion Company Limited.

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.