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In this paper, adaptive neuro-fuzzy inference system ANFIS is used to assess conditions required for aquatic systems to serve as a sink for metal removal; it is used to generate information on the behavior of heavy metals (mercury) in water in relation to its uptake by bio-species (e.g. bacteria, fungi, algae, etc.) and adsorption to sediments. The approach of this research entails training fuzzy inference system by neural networks. The process is useful when there is interrelation between variables and no enough experience about mercury behavior, furthermore it is easy and fast process. Experimental work on mercury removal in wetlands for specific environmental conditions was previously conducted in bench scale at Concordia University laboratories. Fuzzy inference system FIS is constructed comprising knowledge base (<i>i.e.</i> premises and conclusions), fuzzy sets, and fuzzy rules. Knowledge base and rules are adapted and trained by neural networks, and then tested. ANFIS simulates and predicts mercury speciation for biological uptake and mercury adsorption to sediments. Modeling of mercury bioavailability for bio-species and adsorption to sediments shows strong correlation of more than 98% between simulation results and experimental data. The fuzzy models obtained are used to simulate and forecast further information on mercury partitioning to species and sediments. The findings of this research give information about metal removal by aquatic systems and their efficiency

Conventional and neutral high molecular weight polyethylene oxide (PEO) adsorbs on some colloids and fines, flocculating them into flocs. Addition of a cofactor (CF) makes PEO adsorb on all types of colloids and fines, flocculating them into larger flocs. Homoflocculation of fines with PEO alone and with CF added prior to PEO were investigated in this work at low and high effective shear rates. CF role was investigated: it enhanced flocculation amplitude and rate by several magnitudes relative to PEO used alone, and was ascribed to the CF action to stiffen and extend PEO coils. Considering CF-PEO abilities in homoflocculation and in heteroflocculation as recorded in the literature, combination of homo - and heteroflocculation can now be applied to processes. Formed flocs and individual particles will simultaneously deposit onto fibers and, when filtered, particles will be retained in the fiber cake. This technique can be applied in industry processes and water treatment

Biogas in landfill is being captured by natural and engineered processes. The natural processes are represented by biological activities such as bacterial methane oxidation and plant uptake for carbon dioxide at topsoil layer. Landfill gas is transported through soil layers in landfill top or in nearby areas before being released to the atmosphere. Whilst transported in the soil layers the biogas is mixed with atmospheric air and the methane may hence be oxidized by the methanotrophic bacteria in the soil using oxygen from atmosphere. Methane oxidation is affected by different environmental factors such as; temperature, water content, nutrients, substrate and oxygen concentrations. One of the ways to decrease greenhouse emissions in the future is to plant fast growing woody crops thereby sequestering carbon and displacing fossil fuels by harvesting woody biomass for bio-energy, or by storing carbon in long-lived woody products. Plant uptake for carbon dioxide is affected by some parameters such as; CO 2 concentration, nitrogen concentration, water content and temperature. The engineered processes are represented by various physical biogas extractions; gas is collected using network of collection pipes and wells. The gas collection efficiency in landfills is between 40-90%. Landfill gas can be collected by either a passive or an active collection system. Passive gas collection systems use existing variations in landfill pressure and gas concentrations to vent landfill gas into the atmosphere or a control system. Active gas collection is considered a good means of landfill gas collection. An active collection system composed of extraction wells connected to header pipe to a pump that delivers gas for energy recovery.

The aim of this study is to control the performance of wastewater treatment plants for treating inorganic materials. Samples of wastewater were investigated along a year. Fuzzy logic modeling procedures were performed onto investigational data to explore with time the concentrations of inorganics in aeration tanks at two stations in Jordan. Model results show that biological treatment of wastewater is not effective to decrease the concentration of inorganic materials. The concentration of each inorganic material at given time and place is being tracked via Fuzzy system. Sugeno-Fuzzy Inference System (FIS) is herein generated by subtractive clustering. The rule extraction method first uses the subtractive clustering function to determine the number of rules and antecedent membership functions and then uses learning estimation to determine each rule's consequent equations. Training technique is conducted using hybrid learning algorithm. It applies a combination of the least-squares method and the back propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. Intelligent monitoring system is then applied; sensors and data logger system provide inputs to fuzzy logic controller. The fuzzy controller uses the FIS generated from experimental data and then the monitor about certain inorganic compound is achieved. The idea of this study is to track inorganic materials concentration at place and time together in the same model that is handy to check it promptly. It provides dynamic control system that is not only records data about concentrations but also gives a decision to comply with standards.