Advanced Materials Research Vol. 1051

Abstract: Sea water desalination through solar radiation distillation process can achieve low cost and sustainable fresh water for remote dry areas. In conventional passive solar stills, the solar radiation passes through the transparent cover and supplies heat to sea water with limited back reflection. The evaporative heat transfer between the water surface and the glass cover produces the distillate by means of film type condensation at the inner surface of the glass cover. In order to enhance evaporation/condensation phase changes, active solar stills were introduced. In these last, saline water is circulated and put in contact with a heat source which supplies heat to the saline water. With this extra energy, the distillate productivity is increased. In this work, heat supply is assumed to be controlled such that the temperature at the inlet of the still can be adjusted through regulation of the circulating heat transfer fluid rate. Using a modelling based on uniform temperature in each still component, a set of ordinary differential equations was derived. The input variables comprised heat transfer fluid rate, inlet temperature as well as sea water rate and basin depth. Extensive parametric studies were performed after that and optimization of the distilled water yield and rate was discussed.

Abstract: As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, there is lack of a guideline that can clearly illustrate different stages during the semantic process mining. The objective of this study is to present a general framework, which unambiguously expresses the main stages of the semantic process mining. Based on this framework, an example about carbon footprint analysis is used to show the possibility of obtaining advantages from semantic process mining.

Abstract: The Jost solution of the fifth order KdV equation derived from inverse scattering transformation in Gel’fand-Levitan-Marchenko formalism satisfy the both two compatibility equations. Therefore, the soliton solutions to the fifth order KdV equation can be verified theoretically.

Abstract: State of charge (SOC) is very important parameter for monitoring the battery charge and discharge operation and estimating the drive distance of electric vehicle. Especially, with the cycle number increasing, the precision estimation of SOC for battery management system is still not well resolved. Therefore, in this study, aim at accurate sampling of voltage, current and temperature signals based on LTC6803-3 chip, the paper proposed a support vector machine (SVM) optimized by particle swarm optimization (PSO) to improve SOC estimation accuracy. The results demonstrate that the proposed PSO-SVM model has good forecasting performance.

Abstract: Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

Abstract: In order to control a process that has short production cycle and where the product type and specifications change often with conventional shewhart control charts such as and control charts, a new control chart must be applied every time the parameters change . As this is a very inefficient method in terms of the cost and time, CV control chart using coefficient of variation statistics was developed. As CV control chart reflects only the current sample data on control chart, it can be useful when there is a significant change in process. However, it does not respond sensitively to a process that has subtle change or requires a high control level. CV-EWMA control chart was researched to monitor small shifts in CV. This study proposes a way to improve accuracy and precision of population parameter estimation of conventional CV-EWMA control chart and applied it to a control chart before analyzing its performance. As a result, the accuracy and precision of conventional CV-EWMA control chart has been improved and it was verified that the proposed control chart is a proper control chart to control small shifts of CV.

Abstract: Commingling is employed in the petroleum industry to enhance oil recovery and reduce costs. It is of great importance to monitor the production of each oil well oilfields. Nowadays, more and more oilfields use chromatographic fingerprint to estimate single-zone production allocation. In order to insure the efficiency and affectivity of the commingled oil well exploiting, the productivity contribution of every single layer must be acquainted. Kernel partial least squares (KPLS) is a promising regression method for tackling nonlinear systems because it can efficiently compute regression coefficients in high-dimensional feature spaces by means of nonlinear kernel functions. Unlike other nonlinear partial least squares (PLS) techniques KPLS does not entail any nonlinear optimization procedures and has a complexity similar to that of linear PLS. Using the technology of crude oil chromatography fingerprint, an algorithm for predicting productivity contribution based on KPLS is proposed. The validity of the method is proved by laboratory artificial experiments. The maximum absolute error of predicted and real proportion is less than 10%. The model can also be applied to other wells which are similar to those used in the experiment. The experiment results show the prediction model is feasible.

Abstract: Gas emission quantity may forecast the quantity of gas inside the coal, which has important significance for predicting the outburst of gas, but the problem always has not been well solved. Traditional Particle swarm optimization (PSO) algorithm lacks the ability to track the optimal solution while the fitness function changes. An improved algorithm named Time Variant PSO (TVPSO) was proposed to track the optimal solution online. Then it was used to choose the parameters of Least Square Support Vector Machine (LSSVM), which could avoid the man-made blindness and enhance the efficiency of online forecasting. The TVPSO-LSSVM method is based on the minimum structure risk of SVM and the globally optimizing ability of TVPSO to forecast continuously the gas emission quantity of the working face. The method was applied to solve the problem of nonlinear chaos time series prediction. Result shows that the method satisfies the need of online forecasting.

Abstract: The worsening environmental issues have called for more thoroughgoing technological, institutional and social transformations taking place in the world. Green industry is seen as one of them. Investments and research development in green technology and green products are remedial actions to reduce environmental deterioration. The objective of this paper is to examine the factors that influence green purchase behavior among young consumers. A total sample of 204 green product users was collected through self-administrative questionnaires in Melaka, Malaysia. The results show that environmental concern, social influence and government and industry role are three independent variables that have relationship with green purchase behavior among consumers.

Abstract: In this paper we applied the DEA model to the performance evaluation of the agriculture and forestry biomass energy companies. By constructing the corresponding evaluation model and index system, we conducted an empirical analysis with the dates of 8 companies from 2009 to 2012. The results state that the technical efficiency of the companies is low when compared with other industries and though many companies are under the condition of input slacks, their production efficiency still grew incrementally. Finally, this paper put forward countermeasures and suggestions for promoting the development of forestry biomass energy listed companies.