Microwave vacuum drying of pomegranate peel: evaluation of specific energy consumption and quality attributes by response surface methodology and artificial neural network

2022 ◽  
Author(s):  
E.J. Rifna ◽  
Madhuresh Dwivedi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Energy Consumption ◽  
Response Surface ◽  
Specific Energy ◽  
Specific Energy Consumption ◽  
Vacuum Drying ◽  
Pomegranate Peel ◽  
Microwave Vacuum Drying

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

scholarly journals Removal of Pharmaceutical Micropollutants with Integrated Biochar and Marine Microalgae

2020 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Amin Mojiri ◽  
Maedeh Baharlooeian ◽  
Reza Andasht Kazeroon ◽  
Hossein Farraji ◽  
Ziyang Lou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Cell Viability ◽  
Response Surface ◽  
Contact Time ◽  
Marine Microalgae ◽  
Chaetoceros Muelleri ◽  
Mean Squared Errors ◽  
High Concentrations

Using microalgae to remove pharmaceuticals and personal care products (PPCPs) micropollutants (MPs) have attracted considerable interest. However, high concentrations of persistent PPCPs can reduce the performance of microalgae in remediating PPCPs. Three persistent PPCPs, namely, carbamazepine (CBZ), sulfamethazine (SMT) and tramadol (TRA), were treated with a combination of Chaetoceros muelleri and biochar in a photobioreactor during this study. Two reactors were run. The first reactor comprised Chaetoceros muelleri, as the control, and the second reactor comprised Chaetoceros muelleri and biochar. The second reactor showed a better performance in removing PPCPs. Through the response surface methodology, 68.9% (0.330 mg L−1) of CBZ, 64.8% (0.311 mg L−1) of SMT and 69.3% (0.332 mg L−1) of TRA were removed at the initial concentrations of MPs (0.48 mg L−1) and contact time of 8.1 days. An artificial neural network was used in optimising elimination efficiency for each MP. The rational mean squared errors and high R2 values showed that the removal of PPCPs was optimised. Moreover, the effects of PPCPs concentration (0–100 mg L−1) on Chaetoceros muelleri were studied. Low PPCP concentrations (<40 mg L−1) increased the amounts of chlorophyll and proteins in the microalgae. However, cell viability, chlorophyll and protein contents dramatically decreased with increasing PPCPs concentrations (>40 mg L−1).

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