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.

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.

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.

A Low-carbon Operation Optimization Method of ETG-RIES Based on Adaptive Optimization Spiking Neural P Systems

To enhance multi-energy complementarity and foster a low carbon economy of energy resources,this paper proposes an innovative low-carbon operation opti-mization method for electric-thermal-gas regional inte-grated energy systems.To bolster the low-carbon operation capabilities of such systems,a coordinated operation framework is presented that integrates carbon capture devices,power to gas equipment,combined heat and power units,and a multi-energy storage system.To address the challenge of high-dimensional constraint imbalance in the optimization process,a novel low-carbon operation opti-mization method is then proposed.The new method is based on an adaptive single-objective continuous optimiza-tion spiking neural P system,specifically designed for this purpose.Furthermore,simulation models of four typical schemes are established and employed to test and analyze the economy and carbon environmental pollution degree of the proposed system model,as well as the performance of the operation optimization method.Finally,simulation results show that the proposed method not only considers the economic viability of the target integrated energy sys-tem,but also significantly improves the wind power utilization and carbon reduction capabilities.

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.

Study on the application of reinforcement learning in the operation optimization of HVAC system

Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems.

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.

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.

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.

A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.

Desulfurization characteristics of slaked lime and regulation optimization of circulating fluidized bed flue gas desulfurization process--A combined experimental and numerical simulation study

Circulating fluidized bed flue gas desulfurization(CFB-FGD) process has been widely applied in recent years. However, high cost caused by the use of high-quality slaked lime and difficult operation due to the complex flow field are two issues which have received great attention. Accordingly, a laboratory-scale fluidized bed reactor was constructed to investigate the effects of physical properties and external conditions on desulfurization performance of slaked lime, and the conclusions were tried out in an industrial-scale CFB-FGD tower. After that, a numerical model of the tower was established based on computational particle fluid dynamics(CPFD) and two-film theory. After comparison and validation with actual operation data, the effects of operating parameters on gas-solid distribution and desulfurization characteristics were investigated. The results of experiments and industrial trials showed that the use of slaked lime with a calcium hydroxide content of approximately 80% and particle size greater than 40 μm could significantly reduce the cost of desulfurizer. Simulation results showed that the flow field in the desulfurization tower was skewed under the influence of circulating ash. We obtained optimal operating conditions of 7.5 kg·s^(-1)for the atomized water flow, 70 kg·s^(-1)for circulating ash flow, and 0.56 kg·s^(-1)for slaked lime flow, with desulfurization efficiency reaching 98.19% and the exit flue gas meeting the ultraclean emission and safety requirements. All parameters selected in the simulation were based on engineering examples and had certain application reference significance.

Assessing environmental impact:Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources.This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and“cradle-to-gate”as the system boundary.Based on the baseline scenario,a natural gas generator set was added to replace central heating,and the light pipes were expanded to constitute the optimized scenario.The results showed that the key impact categories for both scenarios were global warming,fine particulate matter formation,human carcinogenic toxicity,and human non-carcinogenic toxicity.The overall environmental impact of the optimized scenario was reduced by 68%compared to the baseline scenario.A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios,followed by central heating and natural gas.Therefore,to reduce the environmental impact of network systems,it is necessary to further optimize the grid power structure.The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

1 Introduction.With the continuous growth of the global population,the energy demand continues to increase.However,due to the dominance of fossil fuels in global energy and fossil fuels are non-renewable,it has led to the global energy crisis[1].Besides,the use of fossil fuels will generate a mass of air pollutants(e.g.,carbon dioxide,sulfur dioxide,etc.),which will cause serious environmental pollution,climate change[2],etc.To resolve the aforementioned issues,countries around the world have implemented a variety of measures hoping to fundamentally adjust the global energy structure and achieve sustainable development.Thereinto,“Paris Agreement”reached in 2015 under the framework of“United Nations Framework Convention on Climate Change”aims to control the increase in the average temperature of the globe to within 2°C below preindustrial levels,and thereafter to peak global greenhouse gas emissions as soon as possible,continuously decreasing thereafter[3].United Kingdom plans to reduce the average exhaust emissions of“new cars”to approximately 50–70 g/km by 20230,which is roughly half of what it is now[4].In addition,China proposed a plan at“United Nations General Assembly”in 2020 to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.It is a fact that the whole world is committed to changing the current energy structure,protecting the Earth’s ecology,and achieving global sustainable development[5].

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.

Process optimization of an industrial acetic acid dehydration progress via heterogeneous azeotropic distillation

The simulated process model of the HAc dehydration process under actual overloaded condition was conducted by amending the model of standard condition in our previous work using the process data collected from actual production. Based on the actual process model, the operation optimization analysis of each plant(HAc dehydration column, decanter and NPA recycle column) was conducted using Residue Curve Maps(RCMs),sensitivity analysis and software optimization module. Based on the optimized parameters, the influence of feed impurity MA and the temperature of decanter on the separating effect and energy consumption of the whole process were analyzed. Then the whole process operation optimizing strategy was proposed with the objective that the total reboiler duty Q Total of C-1 and C-3 reaches the minimum value, keeping C-1 and C-3 at their optimized separation parameters obtained above, connecting all the broken recycle and connection streams, and using the temperature of D-1 as operation variable. The optimization result shows that the total reboiler duty Q Total of the whole process can reach the minimum value of 128.32 × 10~6 k J·h^(-1) when the temperature of decanter is 352.35 K, and it can save 5.94 × 10~6 k J·h^(-1), about 2.56 t·h^(-1) low-pressure saturated vapor.