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

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.