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.

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 NOx 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.

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.

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.

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.

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).

大缸径柴油机燃烧系统优化模拟

为提高某缸径200 mm船用发电柴油机的燃油经济性,本文设计了活塞燃烧室和燃油喷射系统的升级方案并进行了模拟优化。升级方案提高了压缩比和燃油喷射压力,采用大径深比浅ω燃烧室配合158°喷油夹角喷油嘴。对不同方案下发动机的缸内工作过程进行了计算流体力学模拟,计算了高压指示功和放热率相位,分析了缸内温度、反应过量空气系数和速度分布及演化。模拟结果表明:升级方案能够提高发动机热效率。增加喷孔数并减小孔径,可以在保持NOx排放基本不变的条件下提高高压指示功4.5%,降低碳烟排放约60%。采用“平顶”浅ω燃烧室与158°喷油夹角喷雾配合,油气混合气快速进入余隙并形成逆时针的漩涡流动,能够加速油气混合和燃烧过程,提高热效率。

近废型稠油油藏火驱效益开发新思路

近废型稠油油藏难以通过常规开发方式有效动用剩余储量,火驱技术为此类油藏进一步提高采收率提供方向。现阶段,火驱技术已趋于成熟,但由于成本投入大、经济性差,火驱技术的推广应用面临困境。通过引入量本利分析法中的盈亏平衡模型和敏感性分析,从多维度的经营视角,揭示不同油价年产油量和成本的平衡关系,明确影响火驱经济性的关键指标,进而推动生产运行优化和决策效能提升。研究结果表明,由于储层前后认识差距大,投入力度大于产出力度,同时管控方向不明确,导致火驱成本有效性低,持续开发经营风险认识不足,影响油藏开发投入决策。研究结果为火驱的经营效益提升指明方向,也为近废型稠油油藏效益开发提供管理新思路。

EGR技术对生物柴油混合燃料发动机性能的影响研究

为缓解柴油掺烧生物柴油导致的NO_(x)排放过高问题,将4190ZLC船用中速柴油机作为试验台架,通过AVL_FIRE软件建立燃烧室模型,并验证该模型准确性。设置生物柴油掺混比0~40%等4组变量,EGR率0~12.5%等4组变量,进行柴油机燃烧、性能和排放分析。结果表明,掺烧适量生物柴油能够降低柴油机缸内温度,降低Soot排放,但同时会导致NO_(x)排放明显升高;EGR的引入能够实现低温燃烧,显著降低NO_(x)排放,但EGR率过高会增加Soot排放。生物柴油掺混比B40、EGR率12.5%组合NO排放比原机降低47.7%,Soot排放质量分数比原机降低98.13%。本文结果将为柴油掺混生物柴油燃烧并结合EGR技术提供一定的研究方向。

基于人工神经网络的甲烷富氧燃烧机理优化

采用带误差传播的直接关系图法、全物种敏感性分析和人工神经网络(ANN)联合方法,以点火延迟时间和CO摩尔分数为优化目标,通过对甲烷富氧燃烧详细机理USC mech2.0的简化和优化,提出了基于人工神经网络的甲烷富氧燃烧优化机理(ANN-OMOC)。甲烷富氧燃烧模拟计算和对比分析的结果表明:相比于甲烷富氧燃烧简化机理FSSA的预测误差,优化机理ANN-OMOC对点火延迟时间、层流火焰速度的预测误差分别从2.53%、24.38%降到0.50%、14.41%;与甲烷富氧燃烧的简化机理DRGEP和FSSA相比,优化机理ANN-OMOC对点火延迟时间、OH摩尔分数峰值和CO摩尔分数峰值的预测结果最佳,其相对误差均在10%以下。

基于ISSA-ELM-NARMAX的垃圾焚烧炉燃烧过程动态建模及多目标优化

为了实现垃圾焚烧发电机组稳定、高效、环保的有效协同,对垃圾焚烧炉燃烧过程进行了多目标优化。提出一种极限学习机(ELM)和带有外部输入的非线性自回归滑动平均模型(NARMAX)相结合的动态建模方法,并通过改进麻雀算法(ISSA)对模型参数进行优化,分别建立了垃圾焚烧炉蒸汽质量流量、锅炉效率和第一烟道温度的动态模型;并将ISSA-ELM-NARMAX模型与传统BP神经网络模型、ELM-NARMAX模型和SSA-ELM-NARMAX模型进行对比,最后基于改进非支配遗传算法(NSGA-II)对燃烧过程进行多目标优化,寻找焚烧炉最佳运行参数。结果表明:基于ISSA-ELM-NARMAX的垃圾焚烧炉燃烧过程动态模型更加精确有效,所提出的控制策略可以为焚烧炉司炉人员提供操作指导。

基于半监督竞争聚类和改进Apriori算法的大型火电机组燃烧优化

为消纳大规模新能源并网,火电机组通过数据挖掘进行燃烧优化时需处理更高维度、更大存量的数据,现有无监督聚类/Apriori算法挖掘效率低不适应机组高灵活性运行要求。针对此问题,在无监督聚类算法中引入约束惩罚因子使之转为半监督聚类以提高聚类效率,并基于划分思想对Apriori算法进行改进以避免冗余规则的产生,提高挖掘效率,形成基于半监督竞争聚类与划分关联规则挖掘结合的新数据挖掘算法。以某电厂660 MW机组为例,用新算法进行数据挖掘,得到各运行参数优化值,建立典型样本库实施燃烧优化,并与改进前算法做对比。结果表明:新算法提高了挖掘效率与存储空间利用率,对于大型火电机组的燃烧优化有一定的实际应用价值。

水泥分解炉SNCR脱硝系统的深度强化学习多目标优化控制研究

选择性非催化还原(selective non-catalytic reduction,SNCR)脱硝过程的工艺参数优化可以有效减少水泥分解炉NO_(x)排放和脱硝运行成本。以某水泥分解炉为研究对象,建立基于LightGBM的NO_(x)浓度预测模型,以脱硝成本和NO_(x)浓度最小化为优化目标,采用深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法对水泥分解炉掺烧污泥协同SNCR脱硝过程的相关工艺参数进行优化控制建模。结果表明,NO_(x)浓度预测模型均方根误差(root mean squared error,RMSE)为6.8,平均绝对百分比误差(mean absolute percentage error,MAPE)为3.48%;采用DDPG算法可以对相关工艺参数进行优化,喷氨量和污泥掺烧量分别为427.87 L/h和9.78 t/h时,NO_(x)排放浓度为225.99 mg/(Nm^(3)),脱硝运行成本为1 747.8元/h。该优化结果与其他优化算法结果和常规工况对比,NO_(x)排放浓度和脱硝运行成本均呈现不同程度下降;对模型进行仿真及效果验证可知,所建立模型能输出合理的喷氨量和污泥掺烧量组合,减少SNCR出口NO_(x)浓度波动,有效降低NO_(x)排放浓度和脱硝成本,可实现对SNCR脱硝系统的多目标优化控制。该结果可为基于智能算法的水泥分解炉SNCR脱硝的多目标优化控制设计提供一定参考。

330 MW旋流对冲锅炉低负荷运行的低NO_(x)燃烧优化

以某热电厂一台330 MW旋流对冲燃煤锅炉为研究对象,通过数值模拟分析了锅炉在150 MW低负荷运行、不同配风比下炉内的流动、传热及NO_(x)排放情况。结果表明,当二次风配比和燃尽风率增大时,NO_(x)的生成趋势并不呈线性变化,而是存在一个最低点,可得到一个NO_(x)排放值最低的最佳配风方式。为了验证该最佳工况的优化效果及实际运行的可行性,又将数值研究中的3个典型工况进行了现场实验。实验结果表明:与原运行工况相比,采用最佳配风方式的实际炉膛出口NO_(x)浓度下降20%,锅炉效率提高0.41%。该配风优化方案经济可行,为同类型工程问题提供了优化思路。

基于数据驱动660 MW循环流化床锅炉多目标燃烧优化

为降低某电厂循环流化床锅炉污染物排放,同时提高锅炉燃烧运行经济性,本文采用数据驱动技术实现循环流化床锅炉多目标燃烧优化。基于改进粒子群优化长短期记忆神经网络建立循环流化床锅炉NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,以相对误差为预测性评估指标以确定最佳网络参数;其次,基于改进粒子群优化长短期记忆神经网络(IPSO-LSTM)、长短期记忆神经网络(LSTM)、广义回归神经网络(GRNN)和反向传播神经网络(BPNN)分别构建NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,通过比较预测性评估指标,证明本文构建预测模型有效性;最后,基于非支配排序遗传算法(NSGA-Ⅱ)获取不同运行工况下循环流化床锅炉燃烧优化调整方案,以降低NO_(x)/SO_(2)排放浓度,同时维持排烟温度稳定性。结果表明:相比优化前,优化后NO_(x)排放浓度平均降低了10.58%,SO_(2)排放浓度平均降低了25.81%,最大降低了650 mg/m^(3),且排烟温度平均降低0.14%。

基于KPCA-FCM工况精简的机组燃烧优化

针对深度调峰下运行工况频繁变动使锅炉燃烧优化参数调整难度增大的问题,提出了一种基于KPCA-FCM工况精简的燃烧优化方法。首先对锅炉实际历史运行数据提取稳态工况后,通过核主成分分析法(KPCA)进行降维,选取贡献率较大的运行参数利用模糊聚类算法(FCM)进行分析完成工况划分,实现对工况的精简。然后对不同的燃烧工况匹配对应的工况簇,调整燃烧参数到该类的最佳运行参数。为了验证该方法的合理性,采用最小二乘支持向量机辨识锅炉燃烧热效率模型。以高低两个工况区间为例进行仿真验证,结果表明提取到的最优运行参数目标值可以使锅炉热效率最高提升0.2%。因此,提出的工况精简方法可有效选取最优运行目标值,为现场运行人员调整运行参数提高锅炉效率提供了合理的数据参考。

多点分布式导向台燃烧室结构优化模拟研究

为了改善柴油机燃烧室内燃油喷雾撞壁和混合气形成情况,提出一种直喷式柴油机多点分布式导向台燃烧室。将这种新燃烧室结构参数化,对7个设计变量进行多参数协同优化。以一台230 mm缸径的中速船用柴油机为基础模拟缸内工作过程,采用拉丁超立方取样的方法从设计空间得到600个样本点,根据模拟结果分析各设计参数对发动机性能的影响,根据不同的优化目标得到3种新燃烧室结构。仿真结果表明:在75%负荷工况下与原机ω型燃烧室相比,Ⅰ型燃烧室的指示油耗率(indicated specific fuel consumption,ISFC)降低1.83%,烟粒(soot)排放量降低86.83%;Ⅱ型燃烧室的ISFC降低0.97%,NO_(x)排放量降低7.44%,soot排放量降低68.26%;Ⅲ型燃烧室的INO_(x)和soot排放量分别降低10.52%和58.08%,ISFC基本不变。