10th International Phytotechnologies Conference Proceedings

The International Phytotechnology Society (IPS) is a nonprofit, worldwide professional society comprised of individuals and institutions engaged in the science and application of using plants to deal with environmental problems. IPS’s mission is to promote research, education, training, and application of those technologies that use plants to deal with problems of environmental contamination, carbon sequestration, alternative fuels, and ecological restoration. IPS is open to all researchers, practitioners, regulators, site owners and interested and concerned individuals who want to promote a natural way to deal with environmental problems. Phytotechnologies are the use of plants to remedy environmental problems. Plants can be used to clean or contain contaminants from soil, sediments, or water. Planted systems can degrade organic pollutants and extract heavy metals. Plants can be used to restore impacted ecosystems, provide biofuel, sequester carbon, improve air quality, and beneficially impact our environment. Advances have been made in research to identify and optimize plant capability to reduce risk and enhance environmental benefits. This Society is devoted to bringing together the science, engineering, and applications of phytotechnologies worldwide.https://digitalcommons.esf.edu/phytocon/1000/thumbnail.jp

Proceedings of the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

This volume contains full papers presented at the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, held in Braga, Portugal, between September 4th and 6th, 2008.FC

Modeling of effluent COD in UAF reactor treating cyanide containing wastewater using artificial neural network approaches

In this study the performance of the upflow anaerobic filter (UAF) reactor treating cyanide was simulated using three different neural network techniques (ANNs) - multi-layer perceptron (MLP) neural network, radial basis neural network (RBNN), and generalized regression neural network (GRNN). The performance of UAF reactor over a period of 130 days at different cyanide concentrations was evaluated with these robust models. Influent chemical oxygen demand (CODin), hydraulic retention time (HRT), and influent cyanide concentration (CNin) were the inputs of the models, whereas the output variable was effluent chemical oxygen demand (CODeff). The models' results were compared with each other using four statistical criteria - root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R2). The results showed that the MLP neural network with Levenberg-Marquardt algorithm was found to be better than the RBNN and GRNN techniques. Crown Copyright © 2010 Published by Elsevier Ltd. All rights reserved