Volume 11, Issue 3 -

This paper presents a modeling of methyl methacrylate (MMA) polymerization with toluene in the presence of azo-bi’s-isobutyronitrile (AIBN) using MATLAB. This work aims to optimize the initial concentration of initiator and the reactor temperature to achieve a maximum monomer conversion in minimum batch time. The optimization of solution polymerization of MMA based on the three-stage polymerization model (TSPM) was performed using ode23t solver. The non-linear polymerization kinetics considered the gel, glass and cage effect to obtain a realistic prediction. The predicted reactor and jacket temperature showed a reasonable agreement with the experimental data, where the error is about 2.7 % and 2.3 %, respectively. The results showed that a maximum monomer conversion of 94 % was achieved at 0.126 kgmol m –3 of the initial concentration of AIBN and 346 K of the initial reactor temperature in 8,951 s (2.5 h).

Natural minerals may contain radionuclides of natural origin of Uranium-238 ( 238 U) and Thorium-232 ( 232 Th) decay series. Similarly, coal like any other minerals found in nature contains trace amount of such naturally occurring radionuclides including Potassium-40 ( 40 K). The generation of electricity by coal fired power plant (CFPP) releases particulates emission to the atmosphere and deposited on the surrounding area that may increase the natural background radiation level within the facility. This paper presents an evaluation of the natural radioactivity concentration found in the particulates emission from a typical CFPP in Malaysia. Standard Gaussian dispersion model approach was used to predict the potential radiological hazards arising from the particulates released from the stack. The predicted maximum ground level particulate (C max ) concentration and downwind distance (X) was 52 µg m –3 and 1,600 m of away from the CFPP, respectively. The air dispersion modelling results recorded that the calculated C max released from the CFPP was found lower than the national and international ambient air quality limits, which means that radiological hazards due to inhalation of natural radionuclides in particulate released to the environment is insignificant. The findings revealed that, this activity does not impose any significant radiological risk to the human population at large and the operation is in compliance with the national legislation and international practice.

Modelling of gas-liquid stirred tanks is very challenging due to the presence of strong bubble-liquid interactions. Depending upon the needs and desired accuracy, the simulation may be performed by considering one-way, two-way, three-way or four-way coupling between the primary and secondary phase. Accuracy of the prediction on the two-phase flow generally increases as the details of phase interactions increase but at the expense of higher computational cost. This study deals with two-way and three-way coupling of gas-liquid flow in stirred tanks which were then compared with results via four-way coupling. Population balance model (PBM) based on quadrature method of moments (QMOM) was implemented in a multi-compartment model of an aerated stirred tank to predict local bubble size. The multi-compartment model is regarded as three-way coupling because the local turbulent dissipation rates and flow rates were obtained from a two-way computational fluid dynamics (CFD) simulation. The predicted two-phase flows and local bubble size showed good agreement with experimental data.

In this study, the extraction of Glycyrrhizic acid (GA) from Glycyrrhiza glabra (licorice) root was investigated by Soxhlet extraction and modified supercritical CO 2 with water as co-solvents and 30 min of static extraction time. The high performance liquid chromatography (HPLC) was used to identify and quantitatively determine the amount of extracted GA recovery of supercritical CO 2 extraction of GA. The extraction recovery was modeled by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Different ANFIS networks (by changing the type of membership functions) were compared with evaluation of networks accuracy in GA recovery prediction and subsequently the suitable network was determined. A three-layer artificial neural network was also developed for modeling of GA extraction from licorice plant root. In this regard, different networks (by changing the number of neurons in the hidden layer and algorithm of network training) were compared with evaluation of networks accuracy in extraction recovery prediction. One-step secant back propagation algorithm with six neurons in hidden layer was found to be the most suitable network and the coefficient of determination (R 2 ) was 98.5 %. Gaussian combination membership function (gauss2mf) using 2 membership function to each input was obtained to be optimum ANFIS architecture with mean square error (MSE) of 0.05,0.17 and 0.07 for training, testing and checking data, respectively.

Mint leaves are aromatic, almost exclusively perennial, rarely annual, herbs. Drying of mint leaves guarantees a longer shelf time while preserving its quality for use in pharmaceutical and food industries. In this study, the drying behavior of Mint leaves in a fluidized bed dryer (FBD) under different drying conditions (fixed, semi-fluidized and fluidized) and 4 temperature levels (30, 40, 50 and 60 °C) was investigated. The experimental data was assessed using semi-theoretical and experimental models. Finally, Page’s model was selected as the best model based on its R 2 , χ 2 and RMSE values. Findings showed that the effective moisture diffusivity of Mint leaves ranged between 0.91 × 10 –11 and 10.41 × 10 –11  m 2 /s. The activation energy was also between 60.82 and 65.59 kJ/mol. The results of statistical analyses also showed that drying time was significantly reduced with increasing air velocity (changing drying bed from fix bed to fluid bed).

A multiple-input and multiple-output (MIMO) model, namely Recursive Orthogonal Least Square (ROLS) based radial basis function (RBF) is developed to estimate product compositions in a batch distillation process from temperature measurements. The process data is generated by simulating the differential equations of the batch distillation process, changing the initial feed composition and boiluprate from batch to batch. Moreover, the reflux ratio is also randomly varied within each batch to represent the exact dynamics of the batch distillation. Temperature and distillate composition is correlated by the RBF trained by ROLS algorithm. A Single RBF network estimate the quality of products in real-time. The results show that ROLS based estimator give correct composition estimations for a batch distillation process. The robustness of the ROLS algorithm and low computational requirement makes the estimator attractive for on-line use.