Meysam Davoody

Analysis and Optimization of Ultrasound-Assisted Alkaline Palm Oil Transesterification by RSM and ANN-GA

In this study, the effects of ultrasound irradiation on transesterification process and characteristics of the synthesized biodiesel were investigated. The study was divided into two parts. In the first part, response surface methodology (RSM) and Central Composite Design (CCD) were employed to design experiments, develop the regression model, and evaluate individual and interactive impacts of five independent operational variables. The obtained results were then predicted by an optimized artificial neural network-genetic algorithm (ANN-GA) algorithm. The estimated results were compared with the experimental results. In the second part of the work, the impact of ultrasound irradiation on the main characteristics of the synthesized biodiesel was investigated. The analysis of the operating conditions indicated that reaction temperature and MeOH:oil molar ratio were the most important variables on reaction yield. The experimental results showed that there was a change in the main properties of the synthesized palm oil biodiesel with the density changed by about 0.3 kg/m3, viscosity by 0.12 mm2/s, pour/cloud point by 1–2°C, and flash point by 5°C, depending on different combinations of operational parameters. Besides, the numerical optimization technique was employed to optimize process variables in order to obtain the maximum FAME content (reaction yield) along with the best properties using both RSM and ANN-GA techniques. The maximum reaction yields of 95.2% and 95.1% were predicted by the RSM and ANN-GA models, respectively, at the optimum conditions. The conditions predicted by RSM and ANN-GA proved to be feasible for modeling and optimizing transesterfication yield with an accuracy of 99.18% and 99.14% and biodiesel properties of 98.61% and 98.28%, respectively.

Degradation and Mineralization of Phenol Compounds with Goethite Catalyst and Mineralization Prediction Using Artificial Intelligence

The efficiency of phenol degradation via Fenton reaction using mixture of heterogeneous goethite catalyst with homogeneous ferrous ion was analyzed as a function of three independent variables, initial concentration of phenol (60 to 100 mg /L), weight ratio of initial concentration of phenol to that of H2O2 (1: 6 to 1: 14) and, weight ratio of initial concentration of goethite catalyst to that of H2O2 (1: 0.3 to 1: 0.7). More than 90 % of phenol removal and more than 40% of TOC removal were achieved within 60 minutes of reaction. Two separate models were developed using artificial neural networks to predict degradation percentage by a combination of Fe3+ and Fe2+ catalyst. Five operational parameters were employed as inputs while phenol degradation and TOC removal were considered as outputs of the developed models. Satisfactory agreement was observed between testing data and the predicted values (R2 Phenol = 0.9214 and R2TOC= 0.9082).

Parametric Study and Process Evaluation of Fenton Oxidation: Application of Sequential Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System Computing Technique

The Fenton oxidation is rarely used industrially due to its high operating cost, large chemical consumption, excessive sludge production, and operability only within a narrow pH range. Therefore, there is a need to evaluate the Fenton oxidation to maximize its ability to degrade high-strength dye wastewater at reduced operating cost. Optimization tools are among the most commonly used tool to maximize the degradation of pollutants. The current study aims at evaluating the applicability of response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) to optimize the degradation of Remazol brilliant blue through the Fenton oxidation. The effects of four operating parameters including dye concentration, retention time, and mass ratios of Dye:Fe2+ and H2O2:Fe2+ were evaluated by applying RSM. According to the RSM results, color and chemical oxygen demand (COD) removal of 99.9% and 84%, respectively, were obtained at 120 min at the COD value of 795 mg/L, mass ratios of Dye:Fe2+ = 16, H2O2:Fe2+ = 15 and pH = 3. ANFIS was also used to evaluate the most influential operating parameters on the COD removal based on the RSM results. The ANFIS results showed that the mass ratio of H2O2:Fe2+ had the most significant contribution to the COD removal. High R2 values (≥90%) indicated that the predictions of RSM and ANFIS models for COD removal were acceptable. In conclusion, this study demonstrated that RSM and ANFIS were able to determine the most significant operating parameters and optimum ratios of pollutant:oxidant:catalyst, which reduced the operating cost directly.