Energy management in hybrid electric vehicles using optimized radial basis function neural network

This paper deals with energy management in hybrid electric vehicles. Use of radial basis function neural network (RBFNN) for the problem of energy management gains importance in the present decade. Use of genetic algorithm (GA) and particle swarm optimization (PSO) as optimization algorithms for parameter estimation is also well known. However, none of the researchers in the area tried to use GA and PSO as training algorithms for the problem. Hence in this paper, we propose two novel methods, based on RBFNN. The difference between RBFNN-based approaches in the literature and those used in this paper is the use of GA and PSO (i.e. optimising algorithms) as training algorithm to train RBFNNs. Interestingly, it is seen that the proposed approaches of this paper outperform RBFNN-based approaches in the literature with traditional training.     

人工神经网络方法在资源与环境预测方面的应用

用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明：人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用     

快速工业化区域建设用地问题与制度优化研究——以佛山市南海区为例

快速工业化区域是工业化快速发展、工业化水平迅速提升、经济社会结构急剧变化的新兴地域类型。普遍存在着建设用地总量急剧扩张、空间无序分散、城乡缺乏统筹等问题。本文从佛山市南海区典型案例入手，运用新制度经济学原理。归纳和解析了快速工业化区域城乡建设用地的主要问题，认为双二元管理体制、农村土地产权不完善、城乡土地市场发育不平衡和部门规划不协调等是建设用地发展的主要体制性障碍因素，并从管理、产权、市场、规划等方面系统提出相应的制度优化对策。     

Evaluating efforts to build sustainable WEEE reverse logistics network design: comparison of regulatory and non-regulatory approaches

Many developing countries such as Turkey are still making an effort on building an infrastructure for waste of electrical and electronic equipment (WEEE) reverse logistic network design (RLND) processes. It is obvious that policies/laws/regulations related to WEEE management provide a sustainable framework for implementation in the RLND. The question is here: Does the implementation of WEEE directives make sense in terms of reducing the total cost of the network in the long term? This study aims to compare regulatory and non-regulatory situations of WEEE RLND in developing countries by formulating two models named as ‘regulatory’ and ‘non-regulatory’. Model 1 is considered as sustainable with economic, environmental and social goals, and the quotas imposed by the environmental directive are taken into consideration as the data of product return amount. In Model 2, only economic goal is considered, and product return amount is forecasted using Artificial Neural Network (ANN). A case study is conducted in a recycling company in order to evaluate performance of the proposed models. This study contributes to the relevant literature by (1) comparing the regulatory and non-regulatory situations RL models explicitly and (2) proposing ANN model to forecast EEE product return or WEEE quantity for non-regulatory situation.     

Solid-state controller for an IAG-based stand-alone wind energy conversion system

This paper deals with an implementation of voltage and frequency controller (VFC) for isolated asynchronous generator-based three-phase autonomous wind energy conversion system. The focus of the proposed work is to provide a feasible solution for rural communities to serve their electricity needs. The least mean square algorithm is used for the extraction of active and reactive power components of the load currents. A three-leg voltage-sourced converter with a battery energy storage system is used as a VFC. The control algorithm is implemented using a digital signal processor. The steady-state and dynamic performances of VFC are demonstrated through test results under static and dynamic loads.     

The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

The feasibility of modeling municipal waste generation (MWG) for countries at different levels of development using artificial neural networks (ANN) and selected generic indicators of sustainability was investigated. The main goals of this research were to develop ANN-based models for predicting MWG, to overcome the problem of incomplete MWG data, which is notable in developing countries, and to provide a new method for the planning of municipal solid waste management systems as well as for the simulation of various other scenarios. Data from 26 European countries was used in this study as training, test and validation datasets for the developing of ANN models. Since this kind of modeling is particularly important for developing countries where MWG data is missing or incomplete, emphasis was placed on modeling of MWG for Bulgaria and Serbia. Based on a comparison of actual MWG data with predictions given by the model, we show that ANNs can be applied successfully to modeling and forecasting MWG on a national scale. Moreover, the scope for possible application of the model is broad, since it uses generic indicators of sustainability such as gross domestic product, domestic material consumption and resource productivity, and performs well for countries with highly diversified levels of economic development, industrial structure, productivity and output.     

Comparing environmental product footprint for electronic and electric equipment: a multi-criteria approach

Electronic and electric devices are now applied in most human activities: their diffusion is increasing worldwide; furthermore, most of them are characterized by a high replacement rate due to technological obsolescence. Consequently, environmental problems due to their diffusion are increasing; several aspects are involved from the energy consumption derived from their manufacturing processes and their use phases to their end-of-life (EOL) management. Such legislative (e.g. the European Energy Efficiency directive for household appliances) or voluntary interventions (e.g. based on the ISO standards) have been introduced for such devices: the aim is to incorporate environmental considerations in product design and manufacturing in order to benefit the environment. Some attempts are focusing on defining standardized models for the overall lifecycle including waste management. The aim of this paper is to introduce a reference model for comparing environmental product footprint of electrical and electronic equipment (EEE). All life cycles of EEE will be evaluated: a specific focus is on the EOL management process as their waste management represents a complex problem for developed and developing countries. A multi-criteria decision-making model will be developed based on the well-known analytical hierarchy process (AHP) method: differently from traditional AHP applications, an absolute model has been proposed in order to compare EEE effectively from an environmental point of view. A case study validation regarding large household appliances is proposed.     

Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain,Iran

Groundwater ecosystems have unparalleled environmental value. Accurate modeling of groundwater level (GWL) fluctuations is a vital requirement for the protection of the groundwater ecosystems. The GWL modeling is a challenge due to complexities of the underground geological structure. Among the various modeling methods, artificial intelligence (AI)-based approaches serve as desirable alternatives due to their distinctive and potent properties. One of the most practical AI-based approaches is an artificial neural network (ANN) model. The purpose of the current study was to apply time delay neural networks (TDNN) with different network structures and input delays to model the GWL fluctuations. The variables used in the construction and validation of the models were average weekly GWL from January 2002 to January 2013 in two monitoring sites in Semnan/Sorkheh plain, Iran. The study area is an arid region, where overutilization of groundwater threatens the water security in this area. The computational results of the current research demonstrated that the TDNN model is a practical tool in modeling time-series GWL compared to the other state-of-the-art AI-based approaches. Future studies are recommended to explore application of proposed model for more sustainable and effective Groundwater Resources Management (GWRM).

     

A framework to minimise total energy consumption and total tardiness on a single machine

A great amount of energy is wasted in industry by machines that remain idle due to underutilisation. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimisation of energy consumption objective while making scheduling decisions. To minimise energy consumption, the decision maker has to decide the timing and length of turn off/turn on operation (a setup) and also provide a sequence of jobs that minimises the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimisation problem that minimises total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP‐hard problem, a new greedy randomised multiobjective adaptive search metaheuristic is utilised to obtain an approximate pareto front (i.e. an approximate set of non‐dominated solutions). Analytical Hierarchy Process is utilised to determine the ‘best’ alternative among the solutions on the pareto front. The proposed framework is illustrated in a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

基于ANN的土壤重金属分布和污染评价研究

农田土壤重金属污染与备受关注的农产品安全问题有密切联系，因此对其进行研究意义重大。以江苏省南通市为研究区，利用采样点实测数据，借助神经网络模型(ANN)并结合3S技术对问题进行研究，从而对土壤重金属的空间动态分布进行描述，并对各个空间位点重金属的污染状况进行评价。结果表明，神经网络模型能够智能地学习各个样点的空间位置与该点各重金属含量之间的映射关系和预先设计好的分类评价模式，并能够稳健地对各个空间插值点处的重金属含量和各个位点的重金属污染状况进行预测和评价。结论显示，南通市大部分农田土壤重金属污染较轻，但也存在局部地区的严重污染。结论与实际情况相符，表明神经网络模型可以为农田土壤重金属的研究提供一个新的思路和方法。     

Optimal design of additional sampling pattern for drinking-water quality control

A crucial step in complementary studies of the water quality determination is to design the additional sampling pattern or, in other words, to determine the number and location of the additional samples. Since location and number of samples highly affect decisions’ uncertainties, and because sampling process is quite costly and time-consuming, optimization of the sampling pattern will enhance the efficiency and productivity. Solution of such an optimization problem requires defining an objective function and constraints. There exist many previous studies regarding locating additional samples in other environmental problems wherein the objective function is defined as the minimization of the kriging variance (based on the problem nature), but the point is that kriging variance is not sensitive to local variability. Since manner and extent of small-scale variations are both important and necessary in water quality studies, it is required to resolve this shortcoming of the traditional objective function. Solution is to make use of the combined variance consisting of kriging and local variances. In this study, the applicability and efficiency of the minimization of combined variance as the objective function of the additional sampling was adopted and proved for a salt marsh (east of Iran) on the basis of a simulated annealing-based algorithm. It was shown, practically, that the locational distribution of additional sampling points is quite logical and more compatible with experts’ proposed methods using this objective function (compared to the traditional one).     

Integration of DFE and DFMA for the sustainable development of an automotive component

The recent developments in manufacturing organizations recognize sustainability as an important value addition for survival in the competitive scenario. The design engineers are in search of approaches for creating environmental conscious products. The purpose of this paper is to report a research carried out for ensuring sustainable product design by the integration of Design for Environment (DFE) and Design for Manufacture and Assembly (DFMA) methodologies. In this context, this paper reports a case study carried out in an automotive component. The candidate product is the charge alternator pulley. The existing pulley has been created using Computer Aided Design. Then sustainability analysis was conducted on the existing component for determining environmental impact. This is followed by the engineering analysis of the component using ANSYS. Then conceptual design changes were developed in the proposed product using DFMA concept. Then the environmental impact has been evaluated in terms of carbon footprint, energy consumption and air/water impacts on proposed product. It has been found that the optimized pulley design possesses minimal environmental impact. The result of the case study indicated that the integration of DFE and DFMA concept could initiate new developments in sustainable designs with minimal impact to the environment and it also reduces the product cost.     

Comprehensive emergy evaluation and optimization of corn straw power generation system: a case study

Biomass energy has become an important measure to alleviate ecological environment security and energy supply security in China. Emergy accounting method is used to analyze and evaluate economy, environment, and sustainability of corn straw generation system, which includes corn planting subsystem, collection and transportation subsystem, and corn straw power generation subsystem. The key substances that need to be optimized in the system are identified by using sensitivity analysis. Based on the position of key substances in the system, emergy accounting optimization methodology is conducted. Corresponding optimization design scheme is proposed based on the “3R” (reduce, reuse, and recycle) principle of circular economy. Current study shows that emergy yield ratio, emergy investment ratio, environmental loading ratio, and emergy sustainability index of the corn straw power generation system are 3.69, 2.68, 1.61, and 2.29, respectively, which are better than wind power generation system and thermal power generation system. In addition, it is proposed that the fertilizer alternative plan and the transportation redesign plan can replace 50,000 tons potash and reduce equipment resources by 20% and energy resources input by 30%. Economy and sustainable development of the system is significantly improved. The optimization design method based on the “3R“ principle proposed in this paper can provide a useful reference for the research in the field of resource recycling.     

基于遗传算法和GIS的县域水田种植制度空间布局优化

采用种植模式对县级尺度的作物进行空间配置，能够较好地解决作物空间布局中熟制比例不当、茬口不适宜等问题。种植制度空间布局优化的目的在于通过合理配置种植模式的空间位置，以充分利用并保护当地的自然资源和社会资源。其本质是一种具有自然、社会、耕作制度等多方面影响因素及空间特征的组合优化问题。传统的优化算法解决具有空间特征的组合优化问题效率很低，或者难以解决该类问题。遗传算法是一种新的智能优化算法，通过模拟自然进化过程，利用简单的编码技术和遗传操作高效地解决复杂的组合优化问题。提出了一种以GIS为平台，遗传算法为优化模型的县域种植制度空间布局优化方法。     

Multiobjective optimisation method for life-cycle design of mechanical products

Manufacturing that minimises the exhaustion of natural resources, energy used and deleterious environmental impact is increasingly demanded by societies that seek to protect global environments as much as possible. To achieve this, life-cycle design (LCD) is an essential component of product design scenarios; however, LCD approaches have not been well integrated in optimal design methods that support quantitative decision-making. This study presents a method that yields quantitative solutions through optimisation analysis of a basic product design incorporating life-cycle considerations. We consider two types of optimisation approaches that have different aims, namely, (1) to reduce the use of raw materials and energy consumption and (2) to facilitate the reuse of the product or its parts when it reaches the end of its useful life. We also focus on how the optimisation results differ according to the approach used, from the viewpoint of the 3R concept (Reduce, Reuse and Recycling). Our method obtains optimum solutions by evaluating objectives fitted to each of these two optimisation approaches with respect to the product's life-cycle stages, which are manufacturing, use, maintenance, disposal, reuse and recycling. As an applied example, a simple linear robot model is presented, and Pareto optimum solutions are obtained for the multiobjective optimisation problem whose evaluated objectives are the operating accuracy of the robot and the different life-cycle costs for the two approaches. The characteristics of the evaluated objectives and design variables, as well as the effects of using material characteristics as design parameters, are also examined.     

困境中的出路:丹麦解决能源问题的基本经验及启示

丹麦是世界上公认解决能源问题最好的国家之一。本文在分析丹麦在解决能源问题方面取得的惊人成就的基础上。总结了丹麦政府制定能源发展战略和政策、加强对能源开发和使用等全过程的宏观管理、依靠科技进步不断提高能源效率、大力开发和利用清洁可再生能源、积极推广应用各种节能技术等方面的基本经验和做法。在此基础上。文章就我国如何依靠科技进步解决能源问题提出了建议。     

Happiness as a harmonising path for bringing higher education towards sustainability

This paper is a reflection of an innovative project that complements existing higher education for sustainable development (HESD) approaches within universities by providing an alternative path to embed sustainable lifestyles within the student population. It presents the SLEUTH project; an initiative born out of the need to reduce energy consumption of students at university but that, due to its systemic design, transforms and goes beyond purely energy reduction and brings Happiness as an approach to build sustainable lifestyles. This is the distinct feature of this initiative, proposing it as adding value to existing HESD approaches. The paper describes and discusses the collaborative building process, implementation and results in detail. The experiences gathered during the project are used and analysed through ??participant observation??, ??documents??, ??questionnaires?? and ??theme-ing coding?? techniques as a means to understand the richness and diversity of the data resulting from a combination of formal and informal learning settings. Through empirical evidence, this analysis presents ??real life?? evidence to suggest, firstly the initiative??s design success, its impact on energy consumption and contribution to happiness and sustainable lifestyles. Secondly, a successful example for the contribution to existing HESD frameworks that move away from indoctrination and instead leans towards pro-activeness, self-organisation and voluntary collective action.     

Intelligent power supply management of an autonomous hybrid energy generator

ABSTRACT

This work deals with the optimization of an hybrid energy system used to supply an isolated site. The proposed system combines a wind turbine, a photovoltaic panel, a diesel generator and a battery bank to electrify atypical home. An energy cost-effectiveness approach is adopted in accordance with meteorological data, time profile of energy consumption, and the cost of different alternative systems. A variety of performances is obtained through simulations within the Homer Pro environment. The selection of an optimal combination is based on the maximum integration of renewable energy in the suggested system with a minimum of gas emission. According to the obtained results, the overall cost of the selected installation is about 72,900 €, with 0.415€ the unit cost of a kWh electric energy provided with a contribution of renewable energy of around 86%. Simulations show a technical and financial benefits of the different configurations obtained to supply the target site. To control the proposed hybrid energy system, a supervision algorithm is developed and implemented on TMS320F28027 DSP platform. The proposed energy system aims to take advantages of renewable energy sources and shift to conventional sources only when necessary in order to ensure source autonomy and service continuity.     

Power management for hybrid distributed generation systems

The design of a new power management tool able to manage the power flow from different renewable energy sources is proposed in this paper. PV and wind are the primary power sources for the system, and a fuel cell with electrolyser and batteries are used as reserve. The designed controller purpose is to manage power flows among the different energy systems and to assure a continuous supply of load. Modelling and simulation of the various energy sources of distributed generation (DG) systems including wind turbine (WT), fuel cell-electrolyser (FC), photovoltaic (PV) and battery are developed. The coordination controller is designed based on the criteria of providing the load demand and the excess power is used either to produce hydrogen through electrolyser for the FC or to store it in battery. Simulation is carried out in MATLAB/Simulink environment and the results show satisfactory performance of the coordination scheme to satisfy the load requirements.