In this study, sawdust as a cheap method and abundant raw material was utilized to produce active carbon (SDAC). Physiochemical activation was utilized where potassium hydroxide   used as a chemical activating agent and carbon dioxide was used as a physical activating agent. Taguchi method of experimental design was used to find the optimum conditions of SDAC production. The produced SDAC was characterized using SEM to investigate surface morphology and BET to estimate the specific surface area. SDAC was used in aqueous lead ions adsorption. Adsorption process was modeled statistically and represented by an empirical model. The highest specific surface area of SDAC was 688.3 m2/gm. Langmuir and Freundlich isotherms were used to

   Water hyacinth (Eichhornia crassipes) is a free-floating plant, growing plentifully in the tropical water bodies. It is being speculated that the large biomass can be used in wastewater treatment, heavy steel and dye remediation, as a substrate for bioethanol and biogas production, electrical energy generation, industrial uses, human food and antioxidants, medicines, feed, agriculture, and sustainable improvement. In this work, the adsorption of Congo Red (CR) from aqueous solution onto EC biomass was investigated through a series of batch experiments. The effects of operating parameters such as pH (3-9), dosage (0.1-0.9 g. /100 ml), agitated velocity (100-300), size particle (88-353μm), temperature (10-50˚C), initial dye

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

A new Schiff base ligand Bis-1,4-di[N-3-(2-hydroxy-1-amino)- acetophenonylidene] benzylidene [L] and its complexes with (Mn(II) ,Co(II) ,Ni(II and Cu(II)) were synthesized . The ligand was prepared in two steps. In the first step a solution of (terphthalaldehyde) in methanol reacts under reflux with (p-aminoacetophenone) to give an intermediate compound [1-[3-({4-[(3-Acetyl-phenylimino)-methyl]-benzylidene}-amino)-phenyl]- ethanone which reacts in the second step with (2-Amino-phenol) giving the mentioned ligand. The complexes were synthesized by addition the corresponding metal salt solution to the solution of the ligand in methanol under reflux in (1:1) metal to ligand ratio. On the basis of, molar conductance, I.R., UV-Vis, HPLC, chlorid

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant