This research presents a study for precipitating phosphorus (as phosphate ion) from simulated wastewater (5ppm initial concentration of phosphorus) using calcium hydroxide Ca(OH)₂ solution. The removal of phosphorus by Ca (OH)₂ solution is expected to be very effective since the chemical reaction is of acid-base type but Ca(OH)₂ forms complex compound with phosphate ions called. Hydroxyapatite Ca₅ (PO₄)₃OH. hydroxyapatite is slightly soluble in water. This research was directed towards sustainable elements as phosphorus. Kinetics of the dissolution reaction of hydroxyapatite was investigated to find the best factors to recover phosphorus. The effect of con

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

In this paper flotation method experiments were performed to investigate the removal of lead and zinc. Various parameters such as pH, air flow rate, collector concentrations, collector type and initial metal concentrations were tested in a bubble column of 6 cm inside diameter. High recoveries of the two metals have been obtained by applying the foam flotation process, and at relatively short time 45 minutes . The results show that the best removal of lead about 95% was achieved at pH value of 8 and the best removal of zinc about 93% was achieved
at pH value of 10 by using 100 mg/l of Sodium dodecylsulfate (SDS) as a collector and 1% ethanol as a frother. The results show that the removal efficiency increased with increasing initial m

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

There are still areas around the world suffer from severe shortage of freshwater supplies. Desalination technologies are not widely used due to their high energy usage, cost, and environmental damaging effects. In this study, a mathematical model of single-bed adsorption desalination system using silica gel-water as working pair is developed and validated via earlier experiments. A very good match between the model predictions and the experimental results is recorded. The objective is to reveal the factors affecting the productivity of fresh water and cooling effect in the solar adsorption system. The proposed model is setup for solving within the commercially-available software (Engineering Equation Solver). It is implemented to so

The present study investigated the impact of fuel kind on the emitted emissions at the idling period. Three types of available fuels in Iraq were tested. The tests conducted on ordinary gasoline with an octane number of 82, premium gasoline with an octane number of 92, and M20 (consist of 20% methanol and 80% regular gasoline). The 2 liters Mercedes-Benz engine was used in the experiments.

The results showed that engine operation at idle speed emits high levels of CO, CO₂, HC, NOx and noise. The produced emission levels depend highly on fuel type. The premium gasoline (ON=92) represents the lower emissions level except for noise at all idling speed. Adding methanol to ordinary gasoline (ON=82) showed high levels of emi

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

his study aimed to investigate the usability of Recycled Concrete Aggregate (RCA) in warm mix asphalt (WMA) as the implementation of sustainable construction technology. Five replacement rates (0%, 25%, 50%, 75%, and 100%) were tested for the coarse fraction of virgin aggregate (VA) with 3 types of RCA: untreated RCA, HL-treated RCA, and HCL-treated RCA. Scanning electron microscopy (SEM) analyses were performed to investigate the surface morphology for both treated and untreated RCA. The optimum asphalt cement content for every substitution rate was determined using Marshall mix design method. Thereafter, asphalt concrete specimens were prepared using the optimum asphalt cement content, followed by the evaluation of their performance prope