Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network optimization

The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M€) than the current literature reported best minimum (1.923M€). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.     

Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

Refrigeration waste heat utilization for drying applications: a review

ABSTRACT

Cold chain industry has a vast potential for waste heat recovery. It is a matter of importance for energy efficiency point of view, as global energy demand is increasing day by day. Ample amount of low-grade energy is either unutilized or underutilized. The heat rejected by a Heat pump or refrigeration system emerged as a promising solution for dehydration by utilizing low-grade waste heat despite higher investment. As compared to solar drying technology, heat pump drying evolved as a reliable method regarding better process control, energy efficiency, and quality of the product to be dried. Energy utilized through the refrigeration system’s waste/exhaust heat recovery in combination with or without renewable energy source enhances the overall efficiency of the system and also reduces the cost. This useful review investigated and compared the research findings of waste heat utilization through heat pump and from condenser of refrigeration system on laboratory, pilot as well as industrial scale for drying of various fruits, vegetables, and agro products. Various drying parameters like drying rate, moisture content, Specific Moisture Extraction Rate (SMER), Coefficient of Performance (COP), Exergy efficiency, and temperature as well as humidity conditions inside the drying chamber were also reviewed to promote the technological advancement of energy utilization by commercial cold storage waste heat recovery.     

Accurate power-sharing,voltage regulation,and SOC regulation for LVDC microgrid with hybrid energy storage system using artificial neural network

ABSTRACT

In this paper, an artificial neural network-based control strategy is proposed for low voltage DC microgrid (LVDC microgrid) with a hybrid energy storage system (HESS) to improve power-sharing between battery and supercapacitor (SC) to suit the demand-generation imbalance, maintain state-of-charge (SOC) within boundaries and thereby to regulate the dc bus voltage. The conventional controller cannot track the SCs current rapidly with the high-frequency component that will place dynamic stress on the battery, further resulting in shorter battery life. The significant advantage is that in the proposed control strategy, redirections of unwaged battery currents to SCs for fast compensations enhance battery life span. The proposed control strategy effectiveness was investigated by simulations, including a comparison of overshoot/undershoot and settling time in dc bus voltage with a conventional control strategy. The results have been experimentally verified by hardware-in-loop (HIL) on a field-programmable gate array (FPGA)-based real-time simulator.     

Accumulation of Heavy Metals by in vitro cultures of plants

The aim of the project is to study heavy metals accumulation by the selected plants in both laboratory and field conditions. Within the experiments the aspen (Populus tremula × tremuloides), sunflower (Helianthus annuus) and corn (Zea mays) plants were studied. The reasons for this selection were: a fast growth of these plants, an accumulation capacity and an ability to survive in different types of soils. The study was carried out on the aspen plantlets grown in vitro. The plants were exposed to the aqueous solutions having concentrations 0.1 mM, 0.5 mM of Pb²⁺ or Ni²⁺, respectively. The accumulation capacityfor aspen, was about 70% of Pb²⁺ originally present in the solution. The starting concentration of Pb²⁺ (0.5 mM) exhibited no negative impact on the growth. Besides in vitro expositions, a pilot-scale phytoremediation experiment was carried out at the polluted industrial area (Zn – 75000 mg/kg), (Pb – 16000 mg/kg), (Cr – 590 mg/kg), (Cd – 90 mg/kg) and (Cu – 1700 mg/kg).     

COMBINED SURFACE WATER-GROUNDWATER ANALYSIS OF HYDROLOGICAL SYSTEMS WITH THE AID OF THE HYBRID COMPUTER1

The solution with the aid of the hybrid computer of the partial and total differential equations for an integrated surface water groundwater system is described. A versatile computing technique has been developed to make a rapid and accurate study of the groundwater response due to varying inputs (deep percolation) or outputs (evapotranspiration) from the groundwater system. Spatial variations in basic vegetation phenomena, such as pattern, and hydrological parameters, are represented by means of a grid network which also allows the input of variable boundary conditions. The model is applied to an area in Columbia, South America which is subject to high water-table conditions. Various reclamation schemes and management practices under conditions of irrigated agriculture are assessed.     

Neural-fuzzy control system application for monitoring process response and control of anaerobic hybrid reactor in wastewater treatment and biogas production

Based on the developed neural-fuzzy control system for anaerobic hybrid reactor (AHR) in wastewater treatment and biogas production, the neural network with backpropagation algorithm for prediction of the variables pH, alkalinity (Alk) and total volatile acids (TVA) at present day time t was used as input data for the fuzzy logic to calculate the influent feed flow rate that was applied to control and monitor the process response at di erent operations in the initial, overload influent feeding and the recovery phases. In all three phases, this neural-fuzzy control system showed great potential to control AHR in high stability and performance and quick response. Although in the overloading operation phase II with two fold calculating influent flow rate together with a two fold organic loading rate (OLR), this control system had rapid response and was sensitive to the intended overload. When the influent feeding rate was followed by the calculation of control system in the initial operation phase I and the recovery operation phase III, it was found that the neural-fuzzy control system application was capable of controlling the AHR in a good manner with the pH close to 7, TVA/Alk < 0.4 and COD removal > 80% with biogas and methane yields at 0.45 and 0.30 m3/kg COD removed.     

蒙脱土/聚丙烯酰胺杂化絮凝剂制备及絮凝性能研究

利用十六烷基三甲基溴化铵(CTAB)与钠基蒙脱土(Na-MMT)的阳离子交换反应制得有机化蒙脱土(O-MMT),通过硝酸铈铵引发丙烯酰胺单体在有机化蒙脱土片层间原位插层聚合制得蒙脱土/聚丙烯酰胺(MMT/PAM)杂化物. 同时,研究了引发剂用量、聚合反应时间和蒙脱土含量对杂化物特性粘数的影响,并用XRD、FTIR、TEM对Na-MMT、O-MMT和MMT/PAM杂化物进行结构表征. 最后实验对比了MMT/PAM杂化物与PAM对三氧化二铬悬浮液的絮凝效果. XRD、TEM结果显示,纳米尺寸大小的蒙脱土均匀地分散在PAM基体中. 絮凝实验结果显示,添加质量分数0.5％~5.0％MMT的MMT/PAM杂化物的絮凝效果优于纯PAM.     

以玉米秸秆为底物的纤维素降解菌与产电菌联合产电的可行性

利用单室空气阴极微生物燃料电池（MFC）反应器,以玉米秸秆为底物,以本实验室筛选和保存的纤维素降解菌Chaetomium sp.和Bacillus sp.,以及纤维素降解混合菌PCS-S和H-C为秸秆降解的生物催化剂,探讨了以汽爆秸秆固体为底物进行微生物产电的可行性.结果表明,在MFC系统内,纤维素降解纯菌和混合菌均能使纤维素降解,但产生的电压很低（<90mV,1000Ω）,升高温度（30～38.5℃）对电压输出无明显影响.单独以生活污水作为菌源不能直接降解秸秆产电.只有将H-C和生活污水（产电菌源）混合作为接种体,MFC才能获得较高的电压输出.此时得到的以汽爆秸秆固体作为底物时的最大功率密度为406mW·m-2,仅比葡萄糖作为底物时所得到的最大功率密度510 mW·m-2低20%.