scholarly journals Rambutan (Nephelium lappaceum) seeds for the treatment of Palm Oil Mill Effluent (POME) and its Feedforward Artificial Neural Network (FANN) modeling

2020 ◽  
Vol 4 ◽  
pp. 1-14
Author(s):  
Mohammad Asad Tariq ◽  
Vasanthi Sethu ◽  
Senthilkumar Arumugasamy ◽  
Anurita Selvarajoo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Operating Conditions ◽  
Palm Oil Mill Effluent ◽  
Bayesian Regularization ◽  
Palm Oil Mill ◽  
Artificial Neural

In the present research, local rambutan seed extract was used as a bio-coagulant for the treatment of palm oil mill effluent (POME). Jar test experiments were conducted to find the optimal operating conditions for the removal of turbidity and total suspended solids from POME. At an optimal pH of 3, bio-coagulant dosage of 600 mg/L and room temperature of 28⁰C, an impressive removal of 65% of total suspended solids and 79% of turbidity was achieved. Along with this, a Feedforward Artificial Neural Network (FANN) was used to model the coagulation mechanism. Three different training algorithms were tested on the FANN, namely the Lavenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient methods. The best training algorithm was found to be Bayesian Regularization, based on the fact that it was in closer agreement with the experiment results and gave very low error percentage. The results of this study suggest that rambutan seeds have potential in being used as a bio-coagulant for POME treatment. Treatment efficiencies were reasonably high, and less sludge was produced using this natural treatment method, thus deemed to be more economical and environmentally friendly.

Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants

2020 ◽  
Vol 7 (2) ◽  
pp. 509-535 ◽  
Author(s):  
Nurul Asyikin Mohd Najib ◽  
Vasanthi Sethu ◽  
Senthil Kumar Arumugasamy ◽  
Anurita Selvarajoo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Palm Oil Mill Effluent ◽  
Palm Oil Mill ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Reduction of Total Suspended Solids, Turbidity and Colour of Palm Oil Mill Effluent using Hybrid Coagulation-Fltrafiltration Process

2018 ◽  
Vol 23 (1) ◽  
Author(s):  
W. Q. Ng ◽  
S. O. Lai ◽  
K. C. Chong ◽  
S. S. Lee ◽  
C. H. Koo ◽  
...  
Keyword(s):  
Palm Oil ◽  
Suspended Solids ◽  
Pure Water ◽  
Total Suspended Solids ◽  
Palm Oil Mill Effluent ◽  
Transmembrane Pressure ◽  
Permeate Flux ◽  
Coagulation Process ◽  
Palm Oil Mill ◽  
Uf Membranes

High consumption and production of palm oil have led to the massive generation of palm oil mill effluent (POME). This study was intended to reduce the total suspended solids (TSS), turbidity and colour using hybrid coagulation-ultrafiltration process. POME was pre-treated with coagulation process using polyaluminium chloride (PAC) and optimization of operating condition for coagulation process was performed. The coagulation results revealed that optimum pH, dosage of coagulant and rapid mixing speed were pH 4, 600 mg/L and 200 rpm, respectively. It achieved the highest percent reduction of TSS, turbidity and colour with 99.74%, 94.44% and 94.60%, respectively. Ultrafiltration (UF) membrane was fabricated using polyethersulfone (PES), polyvinylpyrrolidone (PVP) and titanium dioxide (TiO2­) nanoparticle. Different concentrations ranging from zero and 1.0 wt% of TiO2 nanoparticles were added into the dope solution. The characterization studies of UF membranes confirmed that higher concentration of TiO2 provided higher pure water permeability and more porous structure in the UF membranes. The amount of TiO2 in membrane only affected the permeate flux but had no obvious effects on the reduction of TSS, turbidity and colour. The optimum transmembrane pressure was found to be 3 bar, resulting in the greatest reduction of TSS, turbidity and colour.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

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