scholarly journals Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modeling and Optimization of Pectin Extraction from Banana Peel

2020 ◽  
Author(s):  
ERMIAS GIRMA AKLILU
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Extraction Time ◽  
Banana Peel ◽  
Optimum Conditions ◽  
Promising Alternative ◽  
Degree Of Esterification ◽  
Artificial Neural

Abstract The present study, the influence of three independent variables for extraction of pectin were investigated and optimized using artificial neural network and response surface methodology on the yield and degree of esterification of banana peel pectin obtained using acid extraction method. The results revealed that properly trained artificial neural network model is found to be more accurate in prediction as compared to response surface method model. The optimum conditions were found to be temperature of 82oC, pH of 2 and extraction time of 102 min in the desirable range of the order of 0.977. The yield of pectin and degree of esterification under these optimum conditions was 15.64% and 65.94, respectively. Temperature, extraction time and pH revealed a significant (p < 0.05) effect on the pectin yield and degree of esterification. The extracted banana peel pectin was categorized as high methoxyl pectin, based on the high methoxyl content and degree of esterification. In general, the findings of the study show that banana peel can be explored as a promising alternative for the commercial production of pectin.

scholarly journals Optimising Brewery-Wastewater-Supported Acid Mine Drainage Treatment vis-à-vis Response Surface Methodology and Artificial Neural Network

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1485
Author(s):  
Enoch A. Akinpelu ◽  
Seteno K. O. Ntwampe ◽  
Abiola E. Taiwo ◽  
Felix Nchu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Acid Mine Drainage ◽  
Response Surface ◽  
Mine Drainage ◽  
Sulphate Reduction ◽  
Optimum Conditions ◽  
Acid Mine ◽  
Artificial Neural

This study investigated the use of brewing wastewater (BW) as the primary carbon source in the Postgate medium for the optimisation of sulphate reduction in acid mine drainage (AMD). The results showed that the sulphate-reducing bacteria (SRB) consortium was able to utilise BW for sulphate reduction. The response surface methodology (RSM)/Box–Behnken design optimum conditions found for sulphate reduction were a pH of 6.99, COD/SO42− of 2.87, and BW concentration of 200.24 mg/L with predicted sulphate reduction of 91.58%. Furthermore, by using an artificial neural network (ANN), a multilayer full feedforward (MFFF) connection with an incremental backpropagation network and hyperbolic tangent as the transfer function gave the best predictive model for sulphate reduction. The ANN optimum conditions were a pH of 6.99, COD/SO42− of 0.50, and BW concentration of 200.31 mg/L with predicted sulphate reduction of 89.56%. The coefficient of determination (R2) and absolute average deviation (AAD) were estimated as 0.97 and 0.046, respectively, for RSM and 0.99 and 0.011, respectively, for ANN. Consequently, ANN was a better predictor than RSM. This study revealed that the exclusive use of BW without supplementation with refined carbon sources in the Postgate medium is feasible and could ensure the economic sustainability of biological sulphate reduction in the South African environment, or in any semi-arid country with significant brewing activity and AMD challenges.

scholarly journals Optimization of the electrochemical oxidation of textile wastewater by graphite electrodes by response surface methodology and artificial neural network

2021 ◽  
Author(s):  
Mohammed Saleh ◽  
Rabia Yildirim ◽  
Zelal Isik ◽  
Ahmet Karagunduz ◽  
Bulent Keskinler ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electrochemical Oxidation ◽  
Experimental Results ◽  
Dyeing Wastewater ◽  
Optimum Conditions ◽  
Graphite Electrodes ◽  
Artificial Neural

Abstract In this study, electrochemical oxidation of combed fabric dyeing wastewater was investigated using graphite electrodes. The response surface methodology (RSM) was used to design the experiments via the central composite design (CCD). The planned experiments were done to track color changes and chemical oxygen demand (COD) removal. The experimental results were used to develop optimization models using RSM and the artificial neural network (ANN) and they were compared. The developed models by the two methods were in good agreement with the experimental results. The optimum conditions were found at 150 A/m2, pH 5, and 120 min. The removal efficiencies for color and COD reached 96.6% and 77.69%, respectively. The operating cost at the optimum conditions was also estimated. The energy and the cost of 1 m3 of wastewater required 34.9 kWh and 2.58 US$, respectively. The graphite electrodes can be successfully utilized for treatment of combed fabric dyeing wastewater with reasonable cost.

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

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