A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.     

基于改进的GM(1,1)-Markov链组合模型广东省单位GDP能耗预测

本文以灰色系统理论的GM(1,1)模型和随机过程理论的Markov链模型为基础构建了一个动态GM(1,1)-Markov链组合预测模型。该模型同时利用了GM(1,1)模型对序列趋势因素良好的拟合能力和Markov链模型对残差序列信息的提取能力。为进一步提高该模型的预测精度,用泰勒(Taylor)近似方法和新信息优先的思想对该模型进行了改进。最后,以1991-2014年广东省单位GDP能耗数据实证了该模型的预测效果。     

Effect of Filtrated Osmotic Solution Based on Concentrated Chokeberry Juice and Mint Extract on the Drying Kinetics,Energy Consumption and Physicochemical Properties of Dried Apples

Background: Filtration of osmotic solution affects selective penetration during osmotic dehydration (OD), and after drying is finished, this can influence the chemical composition of the material, which is also modified by OD. Methods: Osmotic dehydration was carried out in filtrated and non-filtrated concentrated chokeberry juice with the addition of mint infusion. Then, this underwent convective drying, vacuum-microwave drying and combined convective pre-drying, followed by vacuum-microwave finishing drying. Drying kinetics were presented and mathematical models were selected. The specific energy consumption for each drying method was calculated and the energy efficiency was determined. Results and Discussion: The study revealed that filtration of osmotic solution did not have significant effect on drying kinetics; however, it affected selective penetration during OD. The highest specific energy consumption was obtained for the samples treated by convective drying (CD) (around 170 kJ·g⁻¹ fresh weight (fw)) and the lowest for the samples treated by vacuum-microwave drying (VMD) (around 30 kJ·g⁻¹ fw), which is due to the differences in the time of drying and when these methods are applied. Conclusions: Filtration of the osmotic solution can be used to obtain the desired material after drying and the VMD method is the most appropriate considering both phenolic acid content and the energy aspect of drying.     

基于连续用电量和缴费时间差的客户信用动态评价模型

加大供电量和提高电费回收效率是供电商增加收益的主要途径,并且用电客户的缴费时间差直接影响着电费回收效率。在用电客户信息较少的情况下,仅仅依赖用电客户的用电量信息和缴费时间差信息,采用扩展的“S型”函数对用电量信息和缴费时间差信息进行整合建模,构建了一个能够度量客户持续用电能力和缴纳电费积极性的客户信用动态评价模型。应用构建的模型对赤峰市宁城县实际用电客户进行信用评价,评价结果与领域专家评价结果具有较高的一致性。     

大数据背景下水厂自动投矾模型研究

根据某市自来水有限责任公司第二水厂的历史矾耗数据，建立矾耗流量关于原水浊度、温度等的动态矾耗模型. 通过对数据进行处理得到10900个合格且净水效果高效的数据，将筛选出的数据分为训练样本集和测试样本集. 在回归拟合中，通过拟合R2的大小将原水浊度划分为“低浊”“中浊”“高浊”3个区间，利用泰勒展开公式的非线性变量代换分别对3个区间建立不同的多项式回归模型，得到预测正确率约为72%，总的矾耗流量值约减少了9.6%的结果；在随机森林模型中，使用10900个合格数据，利用训练样本集，以“原水浊度”“pH值”“原水流量”和“水温”为输入变量，建立包含2000棵决策树的随机森林模型，得到预测正确率约为44. 21%，总的矾耗流量值增加了0.04%的结果. 从模型对合格数据的拟合优度看，随机森林模型比非线性回归模型效果更好；在平均绝对误差、平均绝对偏差百分比等评价指标上，前者均优于后者；但从历史数据检验的结果，模型的可解读性，模型的操作难度和推广角度看，分段二元非线性回归模型的优势更为突出.     

Optimal consumption and investment problem with random horizon in a BMAP model

In this paper, we consider the consumption and investment problem with random horizon in a Batch Markov Arrival Process (BMAP) model. The investor invests her wealth in a financial market consisting of a risk-free asset and a risky asset. The price processes of the riskless asset and the risky asset are modulated by a continuous-time Markov chain, which is the phase process of a BMAP. The possible consumption or investment are restricted to a sequence of random discrete time points which are determined by the same BMAP. The investor has only consumption opportunities at some of these random time points, has both consumption and investment opportunities at some other random time points, and can do nothing at the remaining random time points. The object of the investor is to select the consumption–investment strategy that maximizes the expected total discounted utility. The purpose of this paper is to analyze the impact of the consumption–investment opportunity and the economic state on the value functions and consumption–investment strategies. The general solution and the exact solution under the assumption that the consumption and the terminal wealth are evaluated by the power utility are obtained. Finally, a numerical example is presented.     

The bi-objective Pollution-Routing Problem

The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.     

我国渔业企业经济效益分析研究——以2013年我国上市渔业公司数据为例

随着居民收入水平的提高,我国水产品消费数量不断增加.水产品消费需求的增加,一方面导致水产品养殖企业数量增多,水产品养殖产业初具规模;另一方面也导致了市场竞争压力的增大,使得水产品养殖企业经济效益降低.利用2013年我国上市渔业公司财务数据,基于因子分析法研究了我国上市渔业公司经济效益水平.研究结果显示,2013年大湖股份、国联水产各公共因子及综合因子得分都较高,表明其公司盈利能力、偿债能力及运营能力较强,企业后期发展前景较好.而好当家、武昌鱼的综合因子得分较低,需要引起公司重视.最后针对2013年我国上市渔业公司的整体得分情况,提出了提高我国渔业企业经济效益水平的建议.     

Microwave pyrolysis of straw bale and energy balance analysis

The compressed wheat and corn straw bale were pyrolyzed on a microwave heating device self-designed and built with respect to the time-resolved temperature distribution, mass loss and product properties. Considering scale up and technology promotion of microwave pyrolysis (MWP), the investigations on electricity consumption and energy balance of MWP were carried out emphatically. The results indicated that MWP had obvious advantages over conventional pyrolysis, such as heating rapid and more valuable products obtained. The distribution of pyrolysis products such as gas, liquid and char was close to 1:1:1 due to the medium pyrolysis temperature and the slow heating rate, which was not favorable for the formation of gas and/or liquid products. The content of H₂ attained the highest value of 35 vol.% and syngas (H₂ and CO) was greater than 50 vol.%. The electricity consumption of MWP was between 0.58 and 0.65 kW h (kg straw)⁻¹ and with the increase of microwave power, the electricity consumption required for pyrolysis of unit mass of straw increased. The minimum microwave power for MWP was about 0.371 kW (kg straw)⁻¹ and the proportion of heat loss and conversion loss of electricity to microwave energy occupied in the total input energy was 42%. Data and information obtained are useful for the design and operation of pyrolysis of large-sized biomass via microwave heating technology.