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.

Synergetic optimization operation method for distribution network based on SOP and PV

The integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices that can flexibly control active and reactive power flows.With the exception of active power output,photovoltaic(PV)devices can provide reactive power compensation through an inverter.Thus,a synergetic optimization operation method for SOP and PV in a distribution network is proposed.A synergetic optimization model was developed.The voltage deviation,network loss,and ratio of photovoltaic abandonment were selected as the objective functions.The PV model was improved by considering the three reactive power output modes of the PV inverter.Both the load fluctuation and loss of the SOP were considered.Three multi-objective optimization algorithms were used,and a compromise optimal solution was calculated.Case studies were conducted using an IEEE 33-node system.The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation.Synergetic optimization improves power control capability and flexibility,providing better power quality and PV consumption rate.

Operation optimization strategy of existing residential building energy-saving renovation market:From the perspective of subject behavior

The energy-saving renovation of existing residential buildings is a crucial measure to achieve the strategic goal of energy conservation and emission reduction in China and build ecologically livable cities.This article focuses on the perspective of subject behavior,starting from analyzing the current situation and difficulties of the operation of the energy-saving renovation market for existing residential buildings in China,drawing on the practical experience of the operation of the existing residential building energy-saving renovation market abroad.Based on principles such as systematicity,humanization,feasibility,and sustainability,the article constructs an operation optimization system of the existing residential building energy-saving renovation market from the perspective of subject behavior.In order to provide a reference for the healthy and orderly operation of the existing residential building energy-saving renovation market,this paper proposes implementation strategies for optimizing the operation of the existing residential building energy-saving renovation market.Suggestions are proposed from four aspects:optimizing the market environment,innovating the financing model,building the information sharing platform,and utilizing the synergies of the main subjects.

Long-term operation optimization of circulating cooling water systems under fouling conditions

Fouling caused by excess metal ions in hard water can negatively impact the performance of the circulating cooling water system(CCWS)by depositing ions on the heat exchanger's surface.Currently,the operation optimization of CCWS often prioritizes short-term flow velocity optimization for minimizing power consumption,without considering fouling.However,low flow velocity promotes fouling.Therefore,it's crucial to balance fouling and energy/water conservation for optimal CCWS long-term operation.This study proposes a mixed-integer nonlinear programming(MINLP)model to achieve this goal.The model considers fouling in the pipeline,dynamic concentration cycle,and variable frequency drive to optimize the synergy between heat transfer,pressure drop,and fouling.By optimizing the concentration cycle of the CCWS,water conservation and fouling control can be achieved.The model can obtain the optimal operating parameters for different operation intervals,including the number of pumps,frequency,and valve local resistance coefficient.Sensitivity experiments on cycle and environmental temperature reveal that as the cycle increases,the marginal benefits of energy/water conservation decrease.In periods with minimal impact on fouling rate,energy/water conservation can be achieved by increasing the cycle while maintaining a low fouling rate.Overall,the proposed model has significant energy/water saving effects and can comprehensively optimize the CCWS through its incorporation of fouling and cycle optimization.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

DSS Scheme for Operation Optimization Problem on Power Plants

Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...

Operation optimization mode for nozzle governing steam turbine unit

Based on tests and theoretical calculation an optimum steam admission mode is proposed which can effectively solve the steam-excited vibration.An operation mode jointly considering the valve point and operation load is proposed based on the analysis and study of a large number of unit operation optimization methods.According to the steam-excited vibration that occurs during the optimization process when the nozzle governing steam turbine switches from a single valve to multi-valves a steam admission optimization program is proposed.This comprehensive program considering the steam-excited vibration is applied to a 600 MW steam turbine unit to obtain the optimum sliding pressure curve and the optimum operation mode and the steam-excited vibration is solved successfully.

Influence of Design Margin on Operation Optimization and Control Performance of Chemical Processes

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.

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.

Operation optimization of plugged screen cleanup by rotary water jetting

The rotary water jetting is one of the most important techniques for horizontal well cleanup.The jet flow is used to remove plugging particles from sand control screens to recover their permeability.Currently,the operation optimization of this technique depends mainly on experience due to absence of applicable evaluation and design models for removing plugging materials.This paper presents an experimental setup to simulate the cleanup process of plugged screens by rotary water jetting on the surface and to evaluate the performance of a jetting tool.Using real plugged screens pulled from damaged wells,a series of tests were performed,and the qualitative relationships between the cleanup efficiency and various operational parameters,such as the type of fluids used,flow rate,mode of tool movement,etc.,were obtained.The test results indicated that the cleanup performance was much better when the rotary jetting tool moved and stopped periodically for a certain time than that when it reciprocated at a constant speed.To be exact,it was desirable for the rotary jetting tool to move for 1.5-2 m and stop for 2-4 min,which was called the ＂move-stop-move＂ mode.Good cleanup performance could be obtained at high flow rates,and the flow rate was recommended to be no lower than 550-600 L/min.The test results also indicated that complex mud acid was better than clean water in terms of cleanup performance.Good cleanup efficiency and high screen permeability recovery could be achieved for severely plugged screens.Rotary jetting is preferred for the cleanup of horizontal wells with severely plugged screens,and the screen permeability recovery ratio may reach 20％ if optimized operation parameters were used.

Optimization operation model of electricity market considering renewable energy accommodation and flexibility requirement

The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumption and improve power system flexibility,a bi-level optimal operation model of the electricity market is proposed.A probabilistic model of the flexibility requirement is established,considering the correlation between wind power,photovoltaic power,and load.A bi-level optimization model is established for the multi-markets;the upper and lower models represent the intra-provincial market and inter-provincial market models,respectively.To efficiently solve the model,it is transformed into a mixed-integer linear programming model using the Karush–Kuhn–Tucker condition and Lagrangian duality theory.The economy and flexibility of the model are verified using a provincial power grid as an example.

Peak shaving operation optimization of high proportion new energypower generation considering wind-solar complementationand source-load coupling

To optimize peaking operation when high proportion new energy accesses to power grid,evaluation indexes are proposed which simultaneously consider wind-solar complementation and source-load coupling.A typical wind-solar power output scene model based on peaking demand is established which has anti-peaking characteristic.This model uses balancing scenes and key scenes with probability distribution based on improved Latin hypercube sampling(LHS)algorithm and scene reduction technology to illustrate the influence of wind-solar on peaking demand.Based on this,a peak shaving operation optimization model of high proportion new energy power generation is established.The various operating indexes after optimization in multi-scene peaking are calculated,and the ability of power grid peaking operation is compared whth that considering wind-solar complementation and source-load coupling.Finally,a case of high proportion new energy verifies the feasibility and validity of the proposed operation strategy.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

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.

Daily Operation Optimization of a Residential Molten Carbonate Fuel Cell Power System Using Genetic Algorithm

To decrease the cost of electricity generation of a residential molten carbonate fuel cell （MCFC） power system, multi-crossover genetic algorithm （MCGA）, which is based on ＂multi-crossover＂ and ＂usefulness-based selection rule＂, is presented to minimize the daily fuel consumption of an experimental 10kW MCFC power system for residential application. Under the operating conditions obtained by MCGA, the operation constraints are satisfied and fuel consumption is minimized. Simulation and experimental results indicate that MCGA is efficient for the operation optimization of MCFC power systems.

Intelligent decision support system of operation-optimization in copper smelting converter

An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times.

Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

A Comparison of Arithmetic Operations for Dynamic Process Optimization Approach

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.