Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

稀土灼烧过程温湿度场仿真分析

影响稀土灼烧工艺的因素十分复杂,关系产品质量稳定及能耗,现行工艺存在优化空间。通过剖析灼烧窑中温度和湿度分布状况,运用κ-ε双方程湍流模型、流体传热、多孔介质传热等理论,按特定组分运输模式,建立灼烧过程质量、动量和能量耦合传递数学模型。设置不同边界导入Fluent环境对数学模型进行仿真试验,完成数据处理实现工艺参数优化。结果表明所建模型能准确反映灼烧窑中温湿度场分布及变化,且最终仿真结果与实际灼烧后的产品湿度含量相符合。     

炼油废白土在水泥厂燃煤自备电厂中再利用的实践

本项目主要研究和实现了工业废白土在BAYAH燃煤自备电厂中的再利用,实现变废为宝,减少了环境污染,同时为企业创造了一定的经济效益。针对废白土的特性,对燃料输送系统给煤机、皮带机、煤仓、下料溜子和排渣系统进行了改造。通过修改操作规程消除废白土对锅炉运行的影响。     

NNI-1型C_5/C_6烷烃异构化催化剂长周期运行分析及建议

中国石化海南炼油化工有限公司0.2 Mt/a C5/C6烷烃异构化装置以连续重整装置的拔头油为原料,使用NNI-1催化剂,采用一次通过流程,不设脱异戊烷塔和稳定塔,经设在连续重整装置内的脱丁烷塔稳定处理后作为汽油调合组分。该装置于2006年9月开工投产,截至2015年3月已连续运行3个周期。长周期运行分析结果表明:前两个周期中NNI-1催化剂具有较高的异构化活性及选择性,C5异构化率为60%左右,C6异构化率为80%左右,C6选择性为15%左右,产品辛烷值基本达到技术指标要求(RON≥78);而在第三周期运行中,催化剂积炭增加等原因导致其异构化活性及选择性降低,异构化产品辛烷值提升能力呈现逐步衰减的趋势,提高反应苛刻度已不能弥补催化剂活性下降造成的产品辛烷值降低。为保证装置长周期运行,建议择机停工对催化剂进行再生,或是直接换用与装置原料性质匹配的异构化催化剂。     

无人机在输电线路巡检中的多种应用

无人机技术目前的快速发展使得我们国家输电线路自身的巡检能力得到了持续的提升,基于此本文在对无人机技术给予认识的情况下,在了解了输电线路巡检过程中的使用优势之后,还总结了无人机技术在当前我国线路巡检过程中的应用路径以及应用方法;之后使用案例分析的方式分析了无人机技术在当前输电线路巡检过程中的应用方法。     

亲子乐园空调送风方式的数值模拟研究

大型综合室内亲子乐园属于高大空间,设有游乐设施和游戏的特殊性使得对空间的舒适性要求一致,但是送风气流遇阻严重,室内存在较多气流死角,影响室内空气质量和儿童健康。因此其空调设计不仅需要考虑温度、风速的空间均匀度,还要考虑各点的空气龄和PMV-PPD指标。以天津某亲子乐园为研究对象,利用scSTREAM软件对适用于该房间的辐射供冷加新风、置换通风、混合通风三种空调方式的送风效果进行数值模拟分析,从流场的均匀性、人员的热舒适性等方面对模拟结果进行探讨,研究结果表明辐射供冷加新风方式的空间均匀性和PMV指标最佳,混合通风方式的空气龄最小。     

Science fiction reduces the eeriness of android robots: A field experiment

As suggested by the uncanny valley hypothesis, robots that resemble humans likely elicit feelings of eeriness. Based on the psychological model of meaning maintenance, we expected that the uncanny valley experience could be mitigated through a fictional story, due to the meaning-generating function of narratives. A field experiment was conducted, in which 75 participants interacted with the humanlike robot Telenoid. Prior to the interaction, they either read a short story, a non-narrative leaflet about the robot, or they received no preliminary information. Eeriness ratings were significantly lower in the science fiction condition than in both other conditions. This effect was mediated by higher perceived human-likeness of the robot. Our findings suggest that science fiction may provide meaning for otherwise unsettling future technologies.     

CO压缩机优化运行总结

为确保原CO压缩机在净化系统升级改造项目实施后能安全稳定运行,采取了流程优化、工艺条件论证、设备缺陷排查、联锁指标重新确定等一系列措施。改造措施实施后,CO压缩机出口压力稳定在3.23 MPa左右,有效缓解了前后工序压力波动对压缩机稳定运行的影响,各级水冷却器的换热效果也得到了较大的改善。     

Development of a novel algorithm for optimal capacitor placement in distribution systems

Optimal capacitor placement in distribution systems solved by the hybrid method of CODEQ (called HCODEQ method) is proposed in this work. The concepts of chaotic search, opposition-based learning, and quantum mechanics are used in the CODEQ method to overcome the drawback of parameters selection in the differential evolution (DE). However, a larger population size must be used in the CODEQ method. That is a drawback for all evolutionary algorithms (EAs). To overcome this drawback, acceleration operation and migrating operation are embedded into the CODEQ method, i.e. HCODEQ method. The use of these two operations can increase the convergence speed without decreasing the diversity among individuals. One benchmark function and various-scale capacitor placement systems are used to compare the performance of the proposed method, CODEQ method, DE, simulated annealing (SA), and ant system (AS). Numerical results show that the performance of the HCODEQ method is better than the other methods.