Hot rolling pre-heating furnace model optimization technology

Furnace area is regarded as looper between casting and hot rolling,which is very important for material flow balance and production organization as well as temperature regulation etc.In particular,when dealing with energy saving and emission reduction and heating quality improving,pre;eating furnace is paid more attention.The radical target for preheating furnace is to transfer heating energy at the least cost. As we know,the preheating furnace is a dissipation system to obey conservation of energy law,that is to say that input energy always equals to output energy,in the meantime,the whole energy consuming is not reversible.Therefore increasing the efficiency of using energy is uppermost.In this paper,heating transfer efficiency is analysed and mathematical expression is given based on conservation of energy law.Typical optimal methods to improve preheating furnace transfer efficiency coming from foreign factories are presented.According to these methods,every furnace zone temperatures as control variable,target discharging temperature and temperature difference in slab thickness and the temperature between neighbouring zones as well as zone temporary temperature as restrictions,minimal energy consuming as optimizing target.Baosteel preheating furnace model structure and the complicated mapping relation of control parameter set and state set and aim set are presented.Important basic models in the preheating furnace model system are analysed including temperature tracking model and temperature forecasting model and discharging pacing model and slab heating curve.First slab temperature model structure and its peripheric parameter are introduced;second two pacing models are given including timing pacing mode using fixed discharging interval and mill pacing control mode using mill rolling pacing while Baosteel pacing forecasting model using long term and short term forecasting mode) is given;third a heating curve mathematical model considering heating quality and rolling pace and energy consuming is presented;in the end summary is done and the future way is lighted.Baosteel heating model including slab and billet and steel ingot have been developed,the actual applications show a good effect.The future woks include working procedure saving energy and system saving energy considering " Oder and rule" to achieve system harmony and rhythmization.Baosteel Blooming furnaces scheduling system is very useful for smooth production and saving energy.

Model optimization method and connected-pipe experiment of a liquid fuel ramjet engine

The optimization method of a mathematical model and connected-pipe experimental technique for a test in altitude test facility (ATF) of a liquid fuel ramjet engine was researched.The optimization of the simple mathematical model was divided into two steps.Firstly,using the test engine's geometry configuration size data,a preliminary adjustment was done.Secondly,using experimental test data,the components' experiential coefficients were modified appropriately.Emphasis was laid on the simulation technique of flight condition and parameters measurement method.The experimental technique was applied to a ramjet ATF test successfully.The comparison results show that the optimized-model has higher precision and the nozzle gross thrust difference drops from 12% to about 4%.

3D printing robot:model optimization and image compensation

A novel 7-axis robot for industrial large 3D printing applications is presented in this paper, which is developed by Institute of Robotics and Automatic Information System, Nankai University. Base on the prototype, the mechanism on three-dimensional printing is deeply dissected. By investigating the reason of color distortions in 3D printing, a novel multistep compensation algorithm based on model optimization and image compensation is developed. Finally, the experimental results show the performance of the 3D printing robot platform and the proposed algorithms.

A New Modified GM (1,1) Model: Grey Optimization Model

Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.

Different effects of economic and structural performance indexes on model construction of structural topology optimization

The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of structural topology optimization are also discussed.Furthermore,two structural topology optimization models,optimizing a performance index under the limitation of an economic index,represented by the minimum compliance with a volume constraint（MCVC）model,and optimizing an economic index under the limitation of a performance index,represented by the minimum weight with a displacement constraint（MWDC）model,are presented.Based on a comparison of numerical example results,the conclusions can be summarized as follows：（1）under the same external loading and displacement performance conditions,the results of the MWDC model are almost equal to those of the MCVC model;（2）the MWDC model overcomes the difficulties and shortcomings of the MCVC model;this makes the MWDC model more feasible in model construction;（3）constructing a model of minimizing an economic index under the limitations of performance indexes is better at meeting the needs of practical engineering problems and completely satisfies safety and economic requirements in mechanical engineering,which have remained unchanged since the early days of mechanical engineering.

A dynamic optimization model of an integrated coal supply chain system and its application

Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.

Enterprise resource planning implementation decision & optimization models

To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning （ERP） implementation, combined with program evaluation and review technique （PERT）, some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm （GA） based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.

Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.

Heat and mass transfer model optimization and annual energy efficiency analysis for energy recovery indirect evaporative cooling

Indirect evaporative cooling(IEC)is a kind of high efficiency,energy-saving and environmental protection cooling technology,which has been widely used in data centers and other fields in recent years.In this paper,the optimized two-dimensional non-condensation state model of indirect evaporative cooling was proposed.Meanwhile the computer program was updated to solve the developed mathematical model under variable fresh air conditions.The optimized model was verified by the experimental data,and the maximum deviation was only 4.6%.Based on the modified model and the annual hourly meteorological parameters in Tianjin,China,it was analyzed the optimal heat transfer area of IEC used as fresh air pre-cooling unit under various air volumes to provide references for system design and equipment selection.Finally,taking an IEC-primary return air conditioning system of a gymnasium as an example,the hourly energy-saving effect of whole year was simulated by the developed IEC model.The simulation results showed that IEC could control the fresh air temperature below 27℃ and the moisture content below 18 g/kg throughout the year,and undertook 102.6% of the total fresh air cooling load.The findings are useful in future system optimization and design of IEC equipment.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Hybrid optimization model and its application in prediction of gas emission

According to the complex nonlinear relationship between gas emission and its effect factors, and the shortcomings that basic colony algorithm is slow, prone to early maturity and stagnation during the search, we introduced a hybrid optimization strategy into a max-rain ant colony algorithm, then use this improved ant colony algorithm to estimate the scope of RBF network parameters. According to the amount of pheromone of discrete points, the authors obtained from the interval of net- work parameters, ants optimize network parameters. Finally, local spatial expansion is introduced to get further optimization of the network. Therefore, we obtain a better time efficiency and solution efficiency optimization model called hybrid improved max-min ant system （H1-MMAS）. Simulation experiments, using these theory to predict the gas emission from the working face, show that the proposed method have high prediction feasibility and it is an effective method to predict gas emission.

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

Optimization Model of Cattle Husbandry for Rural Household in Cambodia

Cambodia is one of the Southeast Asia. With the agricultural market integration, Cambodia rural household is adjusting livestock structure naturally. In order to provide suitable support for agriculture policy, the authors conducted a survey on 204 rural household in Cambodia. This article uses the optimization model, considering rural labor, cattle size, and animal disease risk, to analyze and get optimum result range. The result shows that the more off-farm job opportunity, suitable cattle feed structure, and investment on public health for cattle, the household income in rural Cambodia will increase.

Global optimization for ducted coaxial-rotors aircraft based on Kriging model and improved particle swarm optimization algorithm

To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization(PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.

PARTICLE SWARM OPTIMIZATION BASED ON PYRAMID MODEL FOR SATELLITE MODULE LAYOUT

To improve the global search ability of particle swarm optimization （PSO）, a multi-population PSO based on pyramid model （PPSO） is presented. Then, it is applied to solve the layout optimization problems against the background of an international commercial communication satellite （INTELSAT-Ⅲ） module. Three improvements are developed, including multi-population search based on pyramid model, adaptive collision avoidance among particles, and mutation of degraded particles. In the numerical examples of the layout design of this simplified satellite module, the performance of PPSO is compared to global version PSO and local version PSO （ring and Neumann PSO）. The results show that PPSO has higher computational accuracy, efficiency and success ratio.

Analyzing the Relation Between Earthquakes and Active Faults Based on the Parameter Optimization Model

The gestation and occurrence of strong earthquakes are closely related to fault activity, which is not only revealed by abundant experimentation and seismism but also proved by modern seismology. On the Chinese mainland, the relation between earthquake activity and active faults is one of the bases for partitioning potential seismic sources, analyzing the seismotectoulcs and estimating location of strong earthquakes.Due to the nonuniformity of earth media, instability of observation systems and disturbance of the environment, etc, the variety of observational data is complicated, that is, there is no absolutely ＂normal＂ or ＂abnormal＂, and seismic anomalies can be divided into many mutually exdusive＂ abnormal states＂. In different conditions of combined time-spacestrength, determining seismic anomalies by different monomial forecast methods and its efficiency could be different due to the uncertainty of a precursor itself or complexity of the relationship between a precursor and earthquake gestation. It is very difficult to discover and dispose of this difference in actual application in a ＂two-state＂ model. But in a ＂multi-state＂ model, the difference can be easily reflected and the optimal combination of forecasting parameters for a forecast method can also be determined easily. Based on the ＂multi-state＂ precursory model and the optimization method for parameters of earthquake forecast model under the condition of optimal forecast efficiency, the relationship of the spatial location of earthquake with M ≥ 6.0 and active faults in three seismic belts are analyzed. The results demonstrate that in the Hetao Seismic Belt, seismicity is mostly concentrated in the range of 20 km along the fault, the optimization model can forecast the location of potential earthquakes of M ≥ 6.0 near the faults with a relatively high accuracy and the reliability is 0.5 ; while in the Qilian Mt. Seismic Belt, the reliability only reaches 0.14 when we use the model to estimate earthquakes within 30 km range along the faults. The ＂multi-state＂ precursory model, the efficiency-evaluating model and the parameter selection of individual earthquake forecast model based on optimal efficiency are of certain revelatory and practicable meanings for developing knowledge about precursors, investigating the laws of earthquake preparation and searching for optimal forecasting methods.

Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating

In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing.

Balancing timber production and habitat conservation of Okinawa Rails(Gallirallus okinawae): Application of a harvest scheduling optimization model in subtropical forest in Okinawa, Japan

Finding the right balance between timber production and the management of forest-dependent wildlife species,present a difficult challenge for forest resource managers and policy makers in Okinawa,Japan.A possible explanation of this can be found in the unique nature of the forest management area which is populated with various kinds of rare and endangered species.This issue has been brought to light as a result of the nomination of northern Okinawa Island in 2018 as a candidate for World Natural Heritage site.The nomination has raised public awareness to the possibility of conflicting management objectives between timber extraction and the conservation of habitat for forest-dependent wildlife species.Managing exclusively for one objective over the other may fail to meet the demand for both forest products and wildlife habitat,ultimately jeopardizing the stability of human and wildlife communities.It is therefore important to achieve a better balance between the objective of timber production and conservation of wildlife habitat.Despite the significance of this subject area,current ongoing discussions on how to effectively manage for forest resources,often lack scientific basis to make sound judgement or evaluate tradeoffs between conflicting objectives.Quantifying the effect of these forest management activities on wildlife habitat provides useful and important information needed to make forest management and policy decisions.In this study we develop a spatial timber harvest scheduling model that incorporates habitat suitability index(HSI)models for the Okinawa Rail(Gallirallus okinawae),an endangered avian species found on Okinawa,Japan.To illustrate how the proposed coupling model assembles spatial information,which ultimately aids the study of forest management effects on wildlife habitat,we apply these models to a forest area in Okinawa and conduct a simple simulation analysis.