New immune multiobjective optimization algorithm and its application in boiler combustion optimization

In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.

A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm

For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.

Mechanism and optimization of fuel injection parameters on combustion noise of DI diesel engine

Combustion noise takes large proportion in diesel engine noise and the studies of its influence factors play an important role in noise reduction. Engine noise and cylinder pressure measurement experiments were carried out. And the improved attenuation curves were obtained, by which the engine noise was predicted. The effect of fuel injection parameters in combustion noise was investigated during the combustion process. At last, the method combining single variable optimization and multivariate combination was introduced to online optimize the combustion noise. The results show that injection parameters can affect the cylinder pressure rise rate and heat release rate, and consequently affect the cylinder pressure load and pressure oscillation to influence the combustion noise. Among these parameters, main injection advance angle has the greatest influence on the combustion noise, while the pilot injection interval time takes the second place, and the pilot injection quantity is of minimal impact. After the optimal design of the combustion noise, the average sound pressure level of the engine is distinctly reduced by 1.0 d B(A) generally. Meanwhile, the power, emission and economy performances are ensured.

Reagent optimization for on-line simultaneous polarographic determination of trace amounts of Cu^(2+), Cd^(2+) and Co^(2+) in the presence of anextremely large excess of Zn^(2+)

Reagents are optimized for the simultaneous determination of trace amounts of Cu^(2+), Cd^(2+) and Co^(2+) in zinc sulfate solution, which contains an extremely large excess of Zn^(2+). First, the reagents and their doses for the experiment are selected according to the characteristics of the zinc sulfate solution. Then, the reagent doses are optimized by analyzing the influence of reagent dose on the polarographic parameters(i.e. half-wave potential E_(1/2) and limiting diffusion current I_p). Finally, the optimization results are verified by simultaneously determining trace amounts of Cu^(2+), Cd^(2+) and Co^(2+) in the presence of an extremely large excess of Zn^(2+). The determination results indicate that the optimized reagents exhibit wide linearity, low detection limits, high accuracy and good precision for the simultaneous determination of trace amounts of Cu^(2+), Cd^(2+) and Co^(2+) in the presence of an extremely large excess of Zn^(2+).

NEURAL NETWORK INTELLIGENT SYSTEM FOR THE ON-LINE OPTIMIZATION IN CHEMICAL PLANTS

A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization

Research on Distribution of Electromagnetic Environment around Substations and Optimization Layout of On-line Monitoring

The characteristics and distribution law of electromagnetic environment around substations with different levels of voltage were studied,and the main influencing factors were discussed. Meanwhile,a scheme for locating monitoring points suitable for an on-line monitoring system of electromagnetic environment was proposed.

Boiler combustion optimization based on ANN and PSO-Powell algorithm

To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.

Comparative study of low NO_(x) combustion optimization of a coal-fired utility boiler based on OBLPSO and GOBLPSO

To reduce NO_(x) emissions of coal-fired power plant boilers,this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized opposition-based learning(GOBLPSO)to a low NO_(x) combustion optimization area.Thermal adjustment tests under different ground conditions,variable oxygen conditions,variable operation modes of coal pulverizer conditions,and variable first air pressure conditions were carried out on a 660 MW boiler to obtain samples of combustion optimization.The adaptability of PSO,differential evolution algorithm(DE),OBLPSO,and GOBLPSO was compared and analyzed.Results of 51 times independently optimized experiments show that PSO is better than DE,while the performance of the GOBLPSO algorithm is generally better than that of the PSO and OBLPSO.The median-optimized NO_(x) emission by GOBLPSO is up to 15.8 mg/m^(3) lower than that obtained by PSO.The generalized opposition-based learning can effectively utilize the information of the current search space and enhance the adaptability of PSO to the low NO_(x) combustion optimization of the studied boiler.

Optimization of Flow Parameters for Waste Lubricating Oil Combustion

The global energy demand has continued to skyrocket, exacerbating the already severe energy problem and environmental pollution, prompting researchers to look for alternative energy sources. Exploration of waste lubricating oil (WLO) as an alternative source of fuel has gained prominence among researchers due to its availability at low cost and the potential to generate energy while providing a safer means of disposal. The main challenge with WLO combustion is proper regulation of fuel and oxidizer during combustion to realize a near stoichiometric result. Additionally, WLO has high viscosity, hence preheating of the oil is necessary to lower the viscosity and enhance atomization, for a more efficient combustion process. This paper presents the optimization of flow parameters for combustion of WLO in a burner system by use of response surface methodology (RSM). The effects of air flow rate, injection pressure and fuel flow rate on combustion performance of a WLO burner were investigated. The highest flame temperature recorded was 1200°C at an air flow rate of 1 m3/min, fuel flow rate of 0.08 m3/hr and injection pressure of 20 bar. Tests on physical and chemical properties of WLO were conducted and characterized according to ASTM standard to ascertain its potential as an alternative fuel. The calorific values of WLO from petrol and diesel engines were found to be 41.23 MJ/kg and 42.65 MJ/kg respectively. Therefore, recycling of WLO by utilizing it as a fuel for burners has double benefits of mitigating environmental pollution and harnessing energy for process heating and power generation.

Investigation on the Optimizing Range of CombustionChamber Configuration Parameters of DSCS

Aim To obtain an optimizing range of the main configuration parameters of double swirls combustion system (DSCS) Methods To analyze the influence of DS combustion cham-ber configuration parameters on fuel spray and mixing by means of the fuel jet developmentperiphery charts obtained by the high speed photography with a modeling test device deve-loped by authors,and to examine it by the tests on a single cylinder diesel engine.Resultsand Conclusion The mixing process can be divided into four phases.The optimizing range of the ration of the inner chamber diameter to the cylinder bore,d2/D,is 0.4-0.7; and the outerchamber diameter,d1 the height of the circular ridge to the piston top face,h1,the radius of outer/inner chamber circle,R1,R2 ,the max depth of outer/inner chamber bowl,H1,H2,etc. are also important

STEADY-STATE AND IDLE OPTIMIZA-TION OF INTERNAL COMBUSTION ENGINE CONTROL STRATEGIES FOR HYBRID ELECTRIC VEHICLES

A novel steady-state optimization （SSO） of internal combustion engine （ICE） strategy is proposed to maximize the efficiency of the overall powertrain for hybrid electric vehicles, in which the ICE efficiency, the efficiencies of the electric motor （EM） and the energy storage device are all explicitly taken into account. In addition, a novel idle optimization of ICE strategy is implemented to obtain the optimal idle operating point of the ICE and corresponding optimal parking generation power of the EM using the view of the novel SSO of ICE strategy. Simulations results show that potential fuel economy improvement is achieved relative to the conventional one which only optimized the ICE efficiency by the novel SSO of ICE strategy, and fuel consumption per voltage increment decreases a lot during the parking charge by the novel idle optimization of ICE strategy.

Optimization of kinetic mechanism for hydrogen combustion based on machine learning

The reduced mechanism based on the minimized reaction network method can effectively solve the rigidity problem in the numerical calculation of turbulent internal combustion engine.The optimization of dynamic parameters of the reduced mechanism is the key to reproduce the experimental data.In this work,the experimental data of ignition delay times and laminar flame speeds were taken as the optimization objectives based on the machine-learning model constructed by radial basis function interpolation method,and pre-exponential factors and activation energies of H2 combustion mechanism were optimized.Compared with the origin mechanism,the performance of the optimized mechanism was significantly improved.The error of ignition delay times and laminar flame speeds was reduced by 24.3%and 26.8%,respectively,with 25%decrease in total mean error.The optimized mechanism was used to predict the ignition delay times,laminar flame speeds and species concentrations of jet stirred reactor,and the predicted results were in good agreement with experimental results.In addition,the differences of the key reactions of the combustion mechanism under specific working conditions were studied by sensitivity analysis.Therefore,the machine-learning model is a tool with broad application prospects to optimize various combustion mechanisms in a wide range of operating conditions.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

Progress and key challenges in catalytic combustion of lean methane

As a primary type of clean energy,methane is also the second most important greenhouse gas after CO_(2)due to the high global warming potential.Large quantities of lean methane(0.1–1.0 vol%)are emitted into the atmosphere without any treatment during coal mine,oil,and natural gas production,thus leading to energy loss and greenhouse effect.In general,it is challenging to utilize lean methane due to its low concentration and flow instability,while catalytic combustion is a vital pathway to realize an efficient utilization of lean methane owing to the reduced emissions of polluting gases(e.g.,NOxand CO)during the reaction.In particular,to efficiently convert lean methane,it necessitates both the designs of highly active and stable heterogeneous catalysts that accelerate lean methane combustion at low temperatures and smart reactors that enable autothermal operation by optimizing heat management.In this review,we discuss the in-depth development,challenges,and prospects of catalytic lean methane combustion technology in various configurations,with particular emphasis on heat management from the point of view of material design combined with reactor configuration.The target is to describe a framework that can correlate the guiding principles among catalyst design,device innovation and system optimization,inspiring the development of groundbreaking combustion technology for the efficient utilization of lean methane.

Experimental and mechanistic study on chemical looping combustion of caking coal

Under high-temperature batch fluidized bed conditions and by employing juye coal as the raw material,the present study determined the effects of the bed material,temperature,OC/C ratio,steam flow and oxygen carrier cycle on the chemical looping combustion of coal.In addition,the variations taking place in the surface functional groups of coal under different reaction times were investigated,and the variations achieved by the gas released under the pyrolysis and combustion of Juye coal were analyzed.As revealed from the results,the carbon conversion ratio and rate were elevated significantly,and the volume fraction of the outlet CO_(2)remained more than 92%under the oxygen carriers.The optimized reaction conditions to achieve the chemical looping combustion of Juye coal consisted of a temperature of 900℃,an OC/C ratio of 2,as well as a steam flow rate of 0.5 g·min^(-1).When the coal was undergoing the chemical looping combustion,volatiles primarily originated from the pyrolysis of aliphatic-CH_(3)and-CH_(2),and CO and H_(2)were largely generated from the gasification of aromatic carbon.In the CLC process,H_(2)O and CO_(2)began to separate out at 270℃,CH4 and tar began to precipitate at 370℃,and the amount of CO_(2)was continuously elevated with the rise of the temperature.

On-line Chatter Detection Using an Improved Support Vector Machine

On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.

On-Line Recognition of Handwritten Chinese Characters

For the design of a system for on-line recognition of handwritten Chinesecharacters stroke segment ordering is such a difficult problem no good solution is availableto it yet so far.An optimal stroke segment matching algorithm based on stroke structureinformation is proposed in this paper.Results of experiments are presented so thatcomparison can be made between the proposed algorithm and the stroke segmentmatching method based on the strokes’absoulute coordinates.

Modeling of propane dehydrogenation combined with chemical looping combustion of hydrogen in a fixed bed reactor

A redox process combining propane dehydrogenation(PDH)with selective hydrogen combustion(SHC)is proposed,modeled,simulated,and optimized.In this process,PDH and SHC catalysts are physically mixed in a fixed-bed reactor,so that the two reactions proceed simultaneously.The redox process can be up to 177.0%higher in propylene yield than the conventional process where only PDH catalysts are packed in the reactor.The reason is twofold:firstly,SHC reaction consumes hydrogen and then shifts PDH reaction equilibrium towards propylene;secondly,SHC reaction provides much heat to drive the highly endothermic PDH reaction.Considering propylene yield,operating time,and other factors,the preferable operating conditions for the redox process are a feed temperature of 973 K,a feed pressure of 0.1 MPa,and a mole ratio of H_(2) to C_(3)H_(8) of 0.15,and the optimal mass fraction of PDH catalyst is 0.5.This work should provide some useful guidance for the development of redox processes for propane dehydrogenation.

Parameter Identifiability and Parameter Estimation of a Diesel Engine Combustion Model

In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).

Design and Application of Optimization Software for Substation Operation Mode Based on EMS

This paper proposes a kind of optimization software for substation operation mode, which can not only read data on-line from EMS, but also calculate total loss of substations in parallel operation, split operation or individual operation mode. It can also select the most optimized way and feed the conclusion back to EMS to make substations operate in the most optimized way. The software is suitable for optimization of substation in rural power grid.