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This paper intended to develop a model for predicting the progress in sporulation of Bacillus thuringiensis as an industrially important sporulating bacterium. Three distinct forms of B. thuringiensis can be observed during the fermentation process: vegetative cells, sporangia and mature spores. A mathematical model was developed for estimating the population of these three cell forms of this bacterium. A cell population balance was derived to represent the dynamic behavior of the fermentation process in a fed-batch culture. An unstructured and segregated model was used for this purpose. Experimental data at various oxygen saturation levels (0, 50 and 100 %) were used for validating the model. The model consists of a partial differential equation that describes the distribution of the cell population based on the cell age. This equation was solved by the method of lines in MATLAB. The proposed model can properly describe the cell populations and sporulation development in the mentioned conditions.

In this study, an effort was performed to improve methylacetylene and propadiene (MAPD) hydrogenation process with investigation of operating conditions which significantly influence the reactor performance. For this purpose, the operational parameters including concentration of components in the feedstock as well as hydrogen and diluent (recycle stream) flow rates were optimized via differential evolution (DE) technique to maximize propylene (PR) production, to minimize green oil formation as well as to control PR selectivity. Then a comparison was made between the optimized and non-optimized (design) conditions. The results indicate that in optimal conditions, PR production rate increases 5.3 ton/day, while the green oil formation as well as propane (PN) production, as unwanted byproducts are reduced to 0.05 and 0.85 ton/day in comparison with the design conditions. Reduction of undesirable byproducts and enhancement of propylene production rate, demonstrate superiority of the optimized conditions to design conditions.

Due to environmental limitations and issues, the main goal of this research is modification of conventional Claus sulfur recovery process to decreases sulfur contaminant emission. In this regard, two environmentally friendly alternatives are proposed based on the isothermal concept in reactors. Since Claus reaction is exothermic and reversible, the adiabatic fixed bed reactors in the catalytic section of Claus process are substituted by the isothermal reactors. The furnace and catalytic reactors are modeled based on the mass and energy conservation laws at steady state condition. To prove accuracy of the developed model, the simulation results of conventional process are compared with the available plant data. Then, the optimal condition of modified processes are calculated considering sulfur recovery as the objective function using the Genetic algorithm as a useful method in global optimization. The attainable decision variables are inlet temperature of furnace and reactors, coolant temperature, feed split fraction and air flow rate in the furnace. The simulation results show that H2S conversion in the proposed cases increases about 1.87 % and 1.78 % compared to the conventional process. Generally, the main advantages of proposed structures are higher sulfur recovery and lower sulfur contaminant emission such as COS and CS2 emission.

The optimisation of complex geometries such as that of monolith reactors can be supported by computation and simulation. However, complex boundaries such as those found in multi-channel monoliths where mass and heat transfer of characteristic of the reaction diffusion equation render such simulations of extremely high computational expense. In the first step toward developing a fast-solving hybrid simulation, a detailed CFD simulation was used to obtain the unsteady state, spatial temperature and concentration (and hence reaction rate) profiles for a range of input conditions. The results of the CFD simulation were then accepted as the benchmark to which faster-solving models were measured against to be considered as viable descriptions. The model evaluated here is a modified plug flow with effectiveness factor correction for wall mass-transfer. A close agreement between both temperature and species mole fraction profiles predicted from the modified plug flow model and a detailed CFD model was found with R 2 values of 0.994 for temperature. The time needed to find a converged solution for plug flow model on a n Intel(R) Core(TM) i5-5300U CPU @ 2.30 GHz workstation was found to be 53 seconds in comparison to 1.3 hours taken by a CFD model.

Evaporation is one of the most standard procedures in many industrial processes. Although its importance, this operation is quite expensive and, to minimize costs, it is applied multiple effect evaporators. In terms of process control, this configuration is more susceptible to disturbances and also more complicated to control due to process non-linearity. In this paper, a two effects evaporator in a feedforward pattern of feeding was controlled by different adaptive strategies of regulatory control. It was developed feedback and feedforward-feedback control loops in which their PID and lead-lag control parameters were modified according to a gain scheduling strategy based on process variable value. It was shown that adaptive strategy used in feedback control led to a better performance against non-adaptive control, reducing ISE, IAE and ITAE criteria by 12.60%, 7.86% and 13.98% respectively, but also the settling time. However, the final control element was affected leading to 59.77% increase of control effort (ISU). On the other hand, feedforward-feedback allowed even more disturbance rejection than adaptive feedback. As example, IAE values were reduced by 29.96% and 48.17% for feedforward-feedback using non-adaptive and adaptive lead-lag control, respectively. Although their better performance, both feedforward-feedback loops increased control effort, reaching even 70 times of feedback ISU value.

In recently developed Cativa TM process, acetic acid is produced by methanol carbonylation reaction in which a complex interaction among all the major reaction species including iridium, ruthenium, methyl acetate, methyl iodide and water are observed. In this study, a statistical technique of response surface method (RSM) (which is called historical data design algorithm) is applied to investigate the concentration effects of these species on the carbonylation reaction rate. A quartic equation is fitted to the experimental data, and its suitability is examined by several statistical tests. Lack of fit, model F-value, coefficient of determination (R 2 ), adjusted R-squared (Adj.R 2 ) and predicted R-squared (Pre. R 2 ) are respectively equal to 1.65, 182.73, 0.9822, 0.9768 and 0.9263. The validation of the proposed model is investigated by numerical optimization of the design-expert software. The desirability value of the model prediction is 0.94 that indicates the high agreement between the model prediction and the experimental results. The individual and binary effects of the considered parameters on the carbonylation rate are also investigated through the developed model. The steep slope/ curvature of Ir, Ru and water concentrations in perturbation plot indicates the high sensitivity of carbonylation rate to these species. The interaction effects of Ru and water on carbonylation rate show that at water concentration of 7 w/w %, a steep increase occurs in the reaction rate by increasing Ru promoter. Investigating the simultaneous effects of Ru and Ir concentrations on the carbonylation rate indicates that the increase of Ru to Ir molar ratio promotes the reaction rate by enhancing the lability of [Ir(CO) 2 I 3 Me] - complex and preventing the production of inactive species of [Ir(CO) 2 I 4 ] - in the catalytic cycle.

Research works on membrane technology, particularly molecular separation in solvent-based systems, has increased tremendously in recent years. In order to apply this technology at industrial scale, a suitable mathematical model for process design and optimisation must be developed. In the present study, mathematical models to describe process performance were developed with different levels of complexities. The models were developed based on two general transport mechanisms, pore-flow and solution-diffusion principles. Models with different complexity levels were developed, ranging from simple process models to a combination of transport, mass transfer and osmotic pressure effects. Series of molecular separation experiments were conducted to validate the models and to compare the difference among all models. The experimental system conducted in this study was a mixture of organic dyes in n-Dimethylformamide (DMF) solution, which mimics a typical industrial application where molecular purification in aggressive organic solvent is required. The filtration results obtained from any mathematical models are in good agreement with the experiments. The calculated purity of the organic dyes in the permeate ranging from 99.72 % to 100 % in comparison to 99.76 % from the experiments at 8000 s. The results obtained from this study can potentially be applied for industrial application as a prediction tool without conducting any excessive experiments.

Jatropha curcas oil (JCO) has been recognized as a viable non-edible feedstock for biodiesel production with the focus of achieving lesser reliance on fossil fuels. The aim of this work is to integrate and simulate the production of biodiesel from Jatropha curcas oil by a two-step process; a hydrolysis step and a trans-esterification step. The challenge is then to optimise the feedstock ratios to obtain the minimal water and methanol consumption to give optimal biodiesel yield. For this purpose, steady-state simulation model of a two-step production process of biodiesel from Jatropha curcas oil was prepared using ASPEN Plus V8.8. The response surface methodology (RSM) based on a central composite design (CCD) was used to design optimisation experiments for the research work. From the ANOVA, methanol/oil ratio of the trans-esterification step was found to have a significant effect on the biodiesel yield compared to the water/oil ratio of the hydrolysis step. The linear model developed was shown to be a good predictor of feedstock ratios for biodiesel yield. The surface plot revealed that both feedstock ratios do not show a significant combinatorial effect on each other. Numerical optimisation gave the optimum values of the feedstock ratios as a methanol/oil ratio of 2.667 and a water/oil ratio of 1. The optimisation results also indicated a predicted optimum biodiesel yield of 10.0938 kg/hr.