scholarly journals Artificial Neural Network (ANN) for Optimization of Palm Oil Mill Effluent (POME) Treatment using Reverse Osmosis Membrane

2018 ◽  
Vol 1095 ◽  
pp. 012021
Author(s):  
Muhammad Said ◽  
Muneer Ba-Abbad ◽  
Siti Rozaimah Sheik Abdullah ◽  
Abdul Wahab Mohammad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reverse Osmosis ◽  
Palm Oil ◽  
Palm Oil Mill Effluent ◽  
Reverse Osmosis Membrane ◽  
Palm Oil Mill ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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 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.

scholarly journals The effect of a SECoS in crude palm oil forecasting to improve business intelligence

2020 ◽  
Vol 9 (4) ◽  
pp. 1604-1611 ◽  
Author(s):  
Al-Khowarizmi Al-Khowarizmi ◽  
Ilham Ramadhan Nasution ◽  
Muharman Lubis ◽  
Arif Ridho Lubis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transaction Costs ◽  
Palm Oil ◽  
Business Intelligence ◽  
Business Processes ◽  
Crude Palm Oil ◽  
The Future ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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