ABSTRACT
This work investigated the influence of process variables of extraction temperature (35-55 °C), solid to liquid ratio (1:20-1:50 g/mL) and time (100-200 min) on the total phenolic content (TPC) and yield (EY) of Carica papaya leaves (CPL) extracts using Box-Behnken experimental design available in Design Expert software. Bi-objective process optimization was also carried out using the desirability function algorithm. The optimum process variables were later used to design an integrated process for the production of CPL extracts with the assistance of SuperPro Designer software. Scale-up studies and economic analysis for CPL extracts production were investigated in the range of 0.638-20.431 × 103 kg CPL extracts/y to determine the most economically feasible production capacity based on the minimum unit production cost (UPC) of CPL extracts. The risk and sensitivity analyses of the most economically feasible production scale were carried out using the Monte Carlo simulation in Oracle Crystal Ball software. Process variables had notable influences on the TPC and EY of CPL extracts. The extraction temperature of 35 °C, solid to liquid ratio of 40.25 g/mL and time of 100 min gave the optimum TPC of 74.65 mg GAE/g d.b and EY of 18.76 % (w/w). HPLC results indicated that CPL extracts were rich in gallic, betulinic, chlorogenic, ellagic, ferulic and caffeic acids. The designed integrated process showed similar behavior with the laboratory scale of 0.18758 g CPL extracts/batch. The preliminary techno-economic analysis indicated that plant capacity has a strong dependence on the material & energy demands and process economics. Plant capacity of 19.857 × 103 kg CPL extracts/y possessed the least UPC and was selected as the most economically feasible scale. The certainty of obtaining base case UPC value of 525.21 US$/kg CPL extracts was 75.20%. Sensitivity analysis showed that extracts recovery, CPL/water, centrifuge purchase cost, extraction time, extractor purchase cost and extraction temperature contributed -5.3 %, +42.8%, +4.0%, +47.1%, +0.1%, and +0.5%, respectively to the variance in UPC of CPL extracts.
ABSTRACT
This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 °C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R2) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m2/s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59-54.93 KJ/mol). Rehydration ratio was highest at air velocity×air temperature×thickness (1.5 m/s×70 °C × 3 mm), and lowest at air velocity × air temperature×thickness (0.5 m/s×70 °C × 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.
