The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R of 0.9956 with MSE of 1.66 × 10. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Sulaimania is a City located in Kurdistan region in the north of Iraq. The city is facing a lack of water, and it will reach a very critical condition shortly. One of the potential solutions is to reuse the treated wastewater for non-direct human uses such as irrigation, washing, firefighting, groundwater recharging, and others. There is no sewage treatment plant in the city. The wastewater flows into a stream through some sewer outlets, and that causes big environmental issues. Decentralized wastewater treatment units (DTUs) are suggested to solve the issue. The treated wastewater will be used for the irrigation of the green areas of the city. The selected plant type is Extended Aeration treatment system, which is recommended for residential areas. Specifying the locations of the treatment units is very important from environmental, social and technical aspects. The main objective of this study is to select the best suitable places for the DTUs. Preliminary selections of 134 nominated areas for DTU locations were made in different places in the city. The locations are distributed into 10 groups near the main sewer pipes of the city. A model is created to evaluate those selected locations and eliminate the non-suitable locations by using GIS software integrated with the Analytical Hierarchy Process (AHP). Five criteria were used in the model, which are, (1) The size of the available lands, (2) The distance from the decentralized units to the green areas (3) Population density around the decentralized treatment unit locations, (4) The slope of the land and (5) Depth of the main sewer pipe at the nominated area. In addition, the model adopted two restriction factors, which are: (1) The distance from the decentralized treatment unit to the buildings should not be less than 10 m and (2) The distance between the main sewer pipes and the treatment units are taken to be <50 m. The results of the suitability analysis produced six classes of suitability levels of the nominated areas started from restricted to extremely suitable. The suitability percentages of the 6 classes of the total nominated areas were found to be; 8.5% (6.95 ha) restricted, 0.4 % (0.23 ha) moderately suitable, 12.8% (10.50 ha) suitable, 38.8% very suitable (31.60 ha), 32.2% (26.33 ha) highly suitable and 7.3% (5.92 ha) extremely suitable. Each nominated area has more than one suitability class. Normalized Weighted Average (NWAV) of the suitability level percentage of each nominated area is found. The values of the NWAV are ranged from 0.0 to 1.0, and the selection of final DTUs locations will be for areas that have NWAV larger than 0.5. Optimum 30 suitable locations are selected out of the 134 nominated areas.

Municipal solid waste management became a major challenge due to the population growth, change of lifestyle in Sulaimania city, especially since the urban expansion increased from 2003, and when many villages in the countryside had already became a part of the city. The major part of generating solid waste in Sulaimania disposed in the Tanjaro area (an open dumping area southeast of Sulaimania city) without any treatment or proper landfilling. In this research, the compositions of generating solid waste of Sulaimania city and its properties were determined. Also, in this research, it was found that household waste generation is threatening the environment of Sulaimania city due to the huge amount of the produced leachate of solid waste which flowing into the Tanjaro dumping area and ultimately contaminating groundwater, surface and subsurface soil there. This research covered 12 different subzone areas within 5 major zones across the city a covering nearly 300 house samples. Also collected solid waste samples were categorized into the kitchen, plastic, glass, electronic and some other types of solid wastes. The results showed that the number of houses generating more than 2 kg of solid waste per day is higher than those with less than 1kg generated solid waste. It has also been inferred that the highest composition ratio of household solid waste was kitchen type and reached (63.1%) followed by plastic-type solid waste (10.2%), cardboard (5.4%), cans and other metals (2.8%), Glass waste (2.7%) and E-waste (0.1%), other wastes such as textile, nylon, wood and lethal around (15.7%). Besides that, the results showed that the amount of the waste is seasonally changing, for example, the amount of solid waste from April to October is higher than other months due to increasing human activities for producing solid waste. Furthermore, this study covered a survey of the main medical waste from 25 clinics across Sulaimania city and counted the main waste contributed elements of the medical waste that produced. The variation in the generation rate, varied and excuses for this variation clearly stated. Also, the clinic medical waste generation was also investigated for their constituents and quantity and also the possibility of its segregation and ways to dispose of it. All the clinics private and public ones were investigated and studied for having chimneys and the possibilities of air pollution due to the burning of the medical waste.

Numerous recent studies have assessed the effect of P-Delta on the structures. This paper investigates the effect of P-Delta in seismic response of structures with different heights. For indicating the effect of P-Delta, nonlinear static analysis (pushover analysis) and nonlinear dynamic analysis (Time history analysis) were conducted by using finite element software. The results showing that the P-Delta has a significant impact on the structural behavior mainly on the peak amplitude of building when the height of the structures increased. In addition, comparison has been made between concrete and steel structure.

This work presents the experimental and modeling process for mercury ions removal from water using functionalized multi‐walled carbon nanotube as adsorbent. The modeling procedure has been carried out using nonlinear autoregressive network with an exogenous input (NARX) neural network modeling technique is used for modeling the adsorbent's adsorption capacity using different parameters based on experimental data. The effect of different parameters including mercury ions concentration, pH, amount of adsorbent dosage, and contact time is studied. Three kinetics models such as intraparticle diffusion, pseudo first‐order, and pseudo second order are applied using the experimental and predicted outputs, the pseudo second order was the best to describe. A sensitivity study is conducted using different parameters. Various indicators are applied to examine the accuracy and efficiency of the NARX model such are mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error, relative error (RE), and coefficient of determination (R2). The value of the maximum RE was 3.49%, the R2 was 0.9998, and the MSE was 4.28 × 10−6. Based on the used indicators, the NARX model was capable to predict the adsorbent's adsorption capacity by comparing the NARX model outputs to the experimental results.