A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed

Abstract

Magnetic abrasive finishing (MAF) is one of the advanced finishing processes, which produces a high level of surface quality and is primarily controlled by a magnetic field. This paper study the effect of the magnetic abrasive finishing system on the material removal rate (MRR) and surface roughness (Ra) in terms of magnetic abrasive finishing system for eight of input parameters, and three levels according to Taguchi array (L27) and using the regression model to analysis the output (results). These parameters are the (Poles geometry angle, Gap between the two magnetic poles, Grain size powder, Doze of the ferromagnetic abrasive powder, DC current, Workpiece velocity, Magnetic poles velocity, and Finishi

   The modified Hummers method was applied to prepare graphene oxide (GO) from the graphite powder. Tin oxide nanoparticles with different loading (10-20 wt.%) supported on reduced graphene oxide were synthesized to evaluate the oxidative desulfurization efficiency. The catalyst was synthesized by the incipient wetness impregnation (IWI) technique. Different analysis methods like FT-IR, XRD, FESEM, AFM, and Brunauer-Emmett-Teller (BET) were utilized to characterize graphene oxide and catalysts. The XRD analysis showed that the average crystal size of graphene oxide was 6.05 nm. In addition, the FESEM results showed high metal oxide dispersions on the rGO. The EDX analysis shows the weight ratio of Sn is close to its theoretical weight.

This research investigates manganese (Mn) extraction from Electric Arc Furnace Steel Slag (EAFS) by using the Liquid-liquid extraction (LLE) method. The chemical analysis was done on the slag using X-ray fluorescence, X-ray diffraction, and atomic absorption spectroscopy. This work consisted of two parts: the first was an extensive study of the effect of variables that can affect the leaching process rate for Mn element from slag (reaction time, nitric acid concentration, solid to liquid ratio, and stirring speed), and the second part evaluates the extraction of Mn element from leached solution. The results showed the possibility of leaching  83.5 % of  Mn element from the slag at a temperature of 25°C, nitric acid co

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Groundwater quality investigation has been carried out in the western part of Iraq (west longitude '40°40). The physicochemical analyses of 64 groundwater samples collected from seven aquifers were used in the determination of groundwater characterization and assessment. The concept of spatial hydrochemical bi-model was prepared for quantitative and qualitative interpretation. Hydrogeochemical data referred that the groundwater is of meteoric origin and has processes responsible for observed brackishness. The geochemical facies of the groundwater reveal that none of the anions and cations pairs exceed 50% and there are practically mixtures of multi-water types (such as Ca–Mg–Cl–HCO₃ and Na+K–SO4–Cl water type) as do