Artificially roughened solar air heater: A comparative study

Artificially roughened solar air heater has been topic in research for the last 30 years. Prediction of heat transfer and fluid flow processes of an artificially roughened solar air heater can be obtained by three approaches: theoretical, experimental, and computational fluid dynamics (CFD). This article provides a comprehensive review of the published literature on the investigations of artificially roughened solar air heater. In the present article, an attempt has been made to present holistic view of various roughness geometries used for creating artificial roughness in solar air heater for heat transfer enhancement. This extensive review reveals that quite a lot of work has been reported on design of artificially roughened solar air heater by experimental approach but only a few studies have been done by theoretical and CFD approaches. Finally this article presents a comparative study of thermo-hydraulic performance of 21 different types of artificial roughness geometries attached on the absorber plate of solar air heater in terms of thermo-hydraulic performance parameter. Heat transfer and friction factor correlations developed by various investigators for different types of artificially roughened solar air heaters have also been reported in this article.     

Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

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.     

Detection of phosphorus species in sediments of artificial landscape lakes in China by fractionation and phosphorus-31 nuclear magnetic resonance spectroscopy

Phosphorus (P) pollution in the sediments of seven artificial landscape lakes was studied via fractionation and phosphorus-31 nuclear magnetic resonance ((31)P NMR) spectroscopy. The lake sediments accumulated significant amounts of P from supplementation with reclaimed water and from runoff from the golf course lawns. The differences in total sediment P among lakes were correlated to the varied pollution extent from the lawns. One striking feature of the artificial lake sediments was the insufficiency of NaOH-extracted Al, which plays an important role in avoiding internal P release during anoxia. Another characteristic was the dominance of orthophosphate in the NaOH-EDTA extractants of the sediments, due to the heavy external P pollution. Phytate, considered prevalent in many soils and lake sediments, as well as polyphosphates and phosphonates which have appeared in some natural lake sediments, was not detected. The rank order of present biogenic P species was monoester-P>DNA-P>pyrophosphate>lipid-P.     

Greenhouse gas production and efficiency of planted and artificially aerated constructed wetlands

Greenhouse gas (GHG) emissions by constructed wetlands (CWs) could mitigate the environmental benefits of nutrient removal in these man-made ecosystems. We studied the effect of 3 different macrophyte species and artificial aeration on the rates of nitrous oxide (N₂O), carbon dioxide (CO₂) and methane (CH₄) production in CW mesocosms over three seasons. CW emitted 2-10 times more GHG than natural wetlands. Overall, CH₄ was the most important GHG emitted in unplanted treatments. Oxygen availability through artificial aeration reduced CH₄ fluxes. Plant presence also decreased CH₄ fluxes but favoured CO₂ production. Nitrous oxide had a minor contribution to global warming potential (GWP < 15%). The introduction of oxygen through artificial aeration combined with plant presence, particularly Typha angustifolia, had the overall best performance among the treatments tested in this study, including lowest GWP, greatest nutrient removal, and best hydraulic properties.     

人工湿地生态系统健康评价实证研究

建立了健康评价指标体系,改进了原有评价标准,构建了模糊综合评价模型,对苏州太湖国家旅游度假区一期人工湿地生态系统进行了健康评价,得出了度假区人工湿地健康评价综合评分为0．784,识别了健康制约因子,并提出了相应的调控措施,以期为湿地的二期、三期工程建设提供科学依据。     

人工神经网络在环境科学中的应用研究

以B-P网络为例介绍了人工神经网络模型的应用原理。以开拓人工神经网络在环境科学中的应用范围为目的,综述了几种常用神经网络模型在环境预测与评价2方面的应用性能。结果表明:人与环境关系的科学量度是多方位、多因素的非线性模糊问题,而人工神经网络模型具有解决该类问题的独特优点。针对不同环境问题,优选模型,开拓人工神经网络方法在环境科学中的应用范围,为环境问题的解决提供新的技术手段。     

人工神经网络在生态环境质量评价中的应用

以生态环境指标的各级评价标准作为训练样本，应用人工神经网络建立生态环境质量评价的BP网络模型。用训练好的BP网络对生态环境质量进行评价，并与模糊识别评价法进行比较。结果表明，人工神经网络用于生态环境质量的评价结果与环境质量实况相符。     

Assessing self-organization of plant communities—A thermodynamic approach

Thermodynamics is a powerful tool for the study of system development and has the potential to be applied to studies of ecological complexity. Here, we develop a set of thermodynamic indicators including energy capture and energy dissipation to quantify plant community self-organization. The study ecosystems included a tropical seasonal rainforest, an artificial tropical rainforest, a rubber plantation, and two Chromolaena odorata (L.) R.M. King & H. Robinson communities aged 13 years and 1 year. The communities represent a complexity transect from primary vegetation, to transitional community, economic plantation, and fallows and are typical for Xishuangbanna, southwestern China. The indicators of ecosystem self-organization are sensitive to plant community type and seasonality, and demonstrate that the tropical seasonal rainforest is highly self-organized and plays an important role in local environmental stability via the land surface thermal regulation. The rubber plantation is at a very low level of self-organization as quantified by the thermodynamic indicators, especially during the dry season. The expansion of the area of rubber plantation and shrinkage of tropical seasonal rainforest would likely induce local surface warming and a larger daily temperature range.