Utilization of pyrolytic oil and hydrogen enriched syngas from single feedstock (delonix regia) through pyrolysis process and its influence on performance and emission characteristics in CI engine

The main objective of the present investigation is to conduct the performance, combustion and emission analysis of CI engine operated using hydrogen enriched syngas (pyrolytic gas) and biodiesel (pyrolytic oil) as dual fuel mode condition. Both the pyrolytic oil and syngas is obtained from single feedstock delonix regia fruit pod through pyrolysis process and then pyrolytic oil is converted into biodiesel through esterification. Initially biomass is subjected to thermal degradation at various pyrolysis temperature ranges like 350–600 °C. During the pyrolysis process syngas, pyrolytic oil and char are produced. The syngas is directly used in the CI engine and pyrolytic oil is converted into biodiesel and then used in the CI engine. The pyrolytic oil and syngas is subjected to FTIR and GC/TCD analysis respectively. The syngas analysis confirms the presence of various gases like H₂, CH₄, CO₂, CO and C₂H₄ in different proportions. The various proportions of the syngas is mainly depending upon the reactor temperature and moisture content in the biomass. The syngas composition varies with increase in the temperature and at 400 °C, higher amount of hydrogen is present and its composition are H₂ 28.2%, CO is 21.9%, CH₄ is 39.1% and other gases in smaller amounts. The biodiesel of B20 and syngas of 8lpm produced from the same feedstock are considered as test sample fuels in the CI engine under dual fuel mode operation to study the performance and emission characteristics. The study reveals that BTE has slight increase than diesel of 1.5% at maximum load. On the another hand emission like CO, HC and smoke are reduced by 15%,25% and 32% respectively at full load condition, whereas NO_x emission is increased at all loads in the range of 10–15%. Therefore B20+syngas of 8lpm can be used as an alternative fuel in CI engine without any modification and major products from pyrolysis process with waste biomass is fully used as fuel in the CI engine.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Accuracy of wind energy forecasts in Great Britain and prospects for improvement

The metric representing the wind energy forecast error, when reported as a percent, is calculated quite differently than the error metrics for electricity transmission, electricity load, or in other industries such as manufacturing when they are also reported as a percent. The resulting calculated metric is quite different from what would be reported if the method utilized elsewhere was employed. This paper examines the possible forecast assessment and operational challenges associated with this finding. Concerning the prospects for improvement, the errors reported in MW of energy have a systematic component. With this insight, we developed a model to improve accuracy.     

基于降噪时序深度学习网络的风电功率短期预测方法

利用风电场历史功率数据预测未来一段时间内的风功率,对保障电网安全稳定运行具有重要的意义。本文提出一种基于奇异谱分析SSA(singular spectrum analysis)和长短时记忆LSTM(long-short term memory net⁃work)网络的时序特征预测框架用于短期风功率的预测。首先通过SSA对历史风功率原始数据进行降噪处理,然后经过数据转换之后,以LSTM网络为基础进行预测模型的训练,最后通过某风电场提供的两个风机的历史功率数据进行验证。实验结果表明,奇异谱分析对风电场的历史数据具有良好的降噪性,SSA+LSTM模型在测试数据上取得了较好的预测性能,能够有效进行短期风功率的预测。     

我国上市公司股票评级研究——财务结构模型分析法在股票市场中的应用

目前,我国股票市场上的股票评级尚处于探索阶段,评级时多采用专家法或历史法,而对于影响股票评级的各因素的确定以及因素间的相互作用缺乏定量分析。研究将结构模型应用到我国股票市场的股票评级中,建立关于我国股票评级的财务结构模型;分析各个因素对股票评级的影响;建立股票评级经验模式值。     

射孔完井出砂预测新模型及其在射孔优化中的应用

分析了射孔完井的油层出砂机理，以线弹性理论为基础，考虑变温应力和流体渗流作用的拖曳力影响，建立了一种新的射孔完井出砂预测模型。应用该模型研究射孔方位、射孔参数对油井出砂的影响，结果表明，深穿透射孔弹比大孔半径射孔弹能更好地避免孔道出砂，采用定向射孔能有效地减少油井出砂问题。其结果对砂岩油藏射孔优化设计具有一定指导意义。     

一种智能型的PCB产业需求预测方法

目前需求预测在整个印制电路板产业的生产活动控制中正扮演着越来越重要的角色。分析了影响印制电路板需求的因素和现有的预测方法,提出了一种适用于PCB产业需求预测的有效方法——遗传/BP—神经网络。实验表明该方法能够进一步改善印制电路板预测的准确度和减少生产成本的消耗。     

油气吸收回收系统的研究及工业应用——(Ⅰ)中型试验及结果分析

在蒸发油气吸收回收技术小试研究的基础上，开发出常温常压吸收法油气回收中试装置，并利用已开发的吸收剂AbsFOV-97进行了中试试验。结果表明，当系统进料气、吸收剂AbsFOV-97、汽油的体积流量比为10．0：1．0：0．5、真空泵解吸压力小于13．3kPa时，系统回收率高达97％以上，高于设计指标，且回收汽油的质量满足使用要求。统计数据表明，油气回收系统进料气、尾气中平均油气摩尔质量分别为65．51、48．97g／mol，该值可为油品蒸发损耗及其控制技术的评价提供参考。     

FCC汽油光催化氧化脱硫的实验室研究

采用光催化氧化与液液萃取同时进行的方法，考察了光敏剂十六烷基三甲基溴化铵的用量、pH值、双氧水体积分数和反应时间对脱硫效果的影响。结果表明，在光源为主波长365nm的300W中压汞灯，双氧水体积分数为25％，FCC汽油与双氧水体积比为1：3，总体积为120mL，加入0．20g十六烷基三甲基溴化铵，以7000r／min高速均质5min，pH值为4，光照10h的实验条件下，FCC汽油脱硫率可达91．20％；脱硫后的双氧水及光敏剂可以重复使用，不会造成二次污染。