Intelligent Techniques for Photocatalytic Removal of Pollution in Wastewater

This paper discusses the elimination of C.I. AY23 (Acid Yellow 23) using UV/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques namely NN (neural network) and PSO (particle swarm optimization) modeling are developed. A summed up of 100 data are used to establish the models, wherein introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2 and irradiation time are the four parameters applied as the input variables and elimination of AY23 as the output variable. The comparison among the predicted results by designed models and the experimental data proves that the performance of the NN model is comparatively sophisticated than the PSO model

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced

Application of AI in Modeling of Real System in Chemistry

In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on artificial intelligence-based removal dye prediction methods are discussed

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO2, and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme

ZnO/Mg-Al Layered Double Hydroxides as a Photocatalytic Bleaching of Methylene Orange - A Black Box Modeling by Artificial Neural Network

The paper reports the development of ZnO-MgAl layered double hydroxides as an adsorbent-photo catalyst to remove the dye pollutants from aqueous solution and the experiments of a photocatalytic study were designed and modeled by response surface methodology (RSM) and artificial neural network (ANN). The co-precipitation and urea methods were used to synthesize the ZnO-MgAl layered double hydroxides and FT-IR, XRD and SEM analysis were done for characterization of the catalyst.The performance of the ANN model was determined and showed the efficiency of the model in comparison to the RSM method to predict the percentage of dye removal accurately with a determination coefficient (R2) of 0.968. The optimized conditions were obtained as follows: 600 oC, 120 min, 0.05 g and 20 ppm for the calcination temperature, irradiation time, catalyst amount and dye pollutant concentration, respectively.

Modelling and Analysis of Flow Rate and Pressure Head in Pipelines

Currently, various approaches with several utilities are proposed to identify damage in the pipeline. The pipeline system is modeled in the form of a distributed parameter system, such that the state space related to the distributed parameter system contains infinite dimension. In this paper, a novel technique is proposed to analyze and model the flow in the pipeline. Important theorems are proposed for testing the observability as well as controllability of the proposed model

Insights into the Impacts of Synthesis Parameters on Lignin-based Activated Carbon and Its Application for: Methylene Blue Adsorption

In the current research, lignin was successfully extracted from industrial waste Kraft black liquor using the acid precipitation method. In the following step, powdered carbon was synthesized through the H3PO4-chemical activation method. The effects of synthesis parameters, including activation temperature (T) within the range of 400-600 ⁰C and two H3PO4/Lignin mass ratios (R) of 2 and 3 on activated carbon (AC) structure, were investigated. The physical and morphological properties of the ACs were obtained through BET, SEM, and FTIR analyses. The potential application of ACs was studied by measuring their adsorption capacity in the adsorption process of Methylene blue (MB) from an aqueous solution. The sensitized AC at R=2, and T= 500 ⁰C (AC-2-500) showed the highest specific surface area (1573.31 m2/g) and the pore volume (0.89 cm3/g), as well as the highest adsorption capacity of MB. This adsorbent was applied in the equilibrium adsorption experiments and kinetic description. The results from kinetic experiments and adsorption isotherms indicated that the pseudo-first-order model and Langmuir model were in correspondence with the experimental data most. The maximum adsorption capacity was 188 mg/g. The study proved there is a high potential for the conversion of black liquor to greatly porous Lignin-based adsorbents. Moreover, the considerable maximum adsorption capacity suggested a significant potential of Lignin-based AC for wastewater treatment

Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2 mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200 W) as an efficient method and characterized by FE-SEM, FTIR and N2 adsorption-desorption isotherm. The effect of different parameters such as contact time (0–120 min), initial metal ion concentration (25–200 mg/L), temperature (298, 318 and 333 K), amount of adsorbent (0.002–0.007 g) and the solution's initial pH (1–7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R2 > 0.99). The maximum monolayer adsorption capacity (qm) of NiO/ACNC was 1428.57 mg/L. Thermodynamic parameters (¿G°, ¿H° and ¿S°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R2 > 0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.Postprint (author's final draft