Volume 15, Issue 4

Most of the industrial acid gas removal (AGR) units employ chemical absorption process for the removal of acid gases from the natural gas. In this study, two gas processing plants operational in Saudi Arabia have been selected where two different amines n1amely, diglycolamine (DGA) and monoethanol amine (MDEA) are used to achieve the sweet gas purity with less than 4 ppm of H 2 S. This study performed a feasibility simulation of AGR unit by utilizing the amine blend (DGA+MDEA) for both plants instead of a single amine. The study used a commercial process simulator to analyze the impact of process variables such as amine circulation rate, amine strength, lean amine temperature, regenerator inlet temperature, and absorber and regenerator pressure on the process performance. The results reveal that when the MDEA (0–15 wt. %) is added to DGA, marginal energy savings can be achieved. However, significant operational energy savings can be made when the DGA (0–15 wt. %) is blended with MDEA being the main amine.

The advent of stricter emission standards has increased the importance of aftertreatment devices and the role of numerical simulations in the evolution of better catalytic converters in order to satisfy these emission regulations. In this paper, a 2-D numerical simulation of a single channel of the monolith catalytic converter is presented by using detailed surface reaction kinetics aiming to investigate the chemical behaviour inside the converter. The model has been developed to study the conversion of carbon monoxide (CO) in the presence of propene (C 3 H 6 ) for low-temperature combustion (LTC) engine application. The inhibition effect of C 3 H 6 over a wide range of CO inlet concentrations is investigated. Considering both low and high levels of CO concentration at the inlet, the 2-D model predicted better results than their corresponding 1-D counterparts when compared with the experimental data from literature. It was also observed that C 3 H 6 inhibition at high temperatures was significant, particularly for high concentrations of CO compared to low concentrations of CO at the inlet.

L-Lysine is one of the important amino acid required for humans and animals. It has a high commercial market. Large scale production of this amino acid is essential to meet the commercial demands. Typically, L-lysine is produced by batch fermentation. In the present study, the important process, as well as nutrient parameters such as glucose concentration (g/L), rpm, incubation temperature (°C), pH and incubation time for L-lysine production by Corynebacterium glutamicum ATCC13032, were optimized by a combined approach of response surface methodology (RSM) with artificial neural network (ANN) method. Initially, 32 runs face central composite design was employed. In the first step, the data was analyzed by the RSM and the optimum conditions for L-lysine production were determined. In the second step, the same data was used to train the neural network. A feed-forward neural network with error backpropagation was used. The best network was obtained by optimizing the no of neurons in the hidden layer. From the best network, the optimized weights and predicted responses were used to optimize the conditions of the selected parameters by genetic algorithm (GA). Overall with the combination of RSM-ANN-GA onefold of L-lysine production from Corynebacterium glutamicum ATCC 13032 was improved.

Extractive distillation uses a high-boiling point solvent for changing the relative volatility of the azeotropic mixture, whereas pressure-swing distillation is based on the difference of operating pressures for such a purpose. In this paper, said separation technologies were applied to a tetrahydrofuran/ethanol mixture and compared with regard to their thermodynamic and environmental performances. The former was assessed by determining the total exergy destruction rate and rational efficiency of each configuration, while the latter was evaluated by estimating their respective indirect carbon emissions. The results showed that the pressure-swing process has not only the lowest exergy destruction rate (383.1 kW) but also the lowest CO 2 emission rate (678.7 kg/h), which is mainly due to its lower thermal energy requirements. A sensitivity analysis was then carried out in order to determine how the carbon emissions respond to both the efficiency and the fuel type of the utility boiler.

Multi-objective optimization is one of the most effective tools for the decision support system. This study aims to optimize the gasification of municipal solid waste (MSW) for advanced power plant. MSW gasifier is simulated using Aspen Plus v11 to produce syngas, to be fed into power generation technologies. Four power generation technologies are selected, solid oxide fuel cell, gas turbine, gas engine, and steam turbine. Mixed-integer non-linear programming (MINLP) multi-objective optimization is developed to provide an optimal solution for minimum levelized cost of electricity (LCOE) and minimum CO 2eq emissions. The optimization is conducted with a ε-constraint method using GAMS through time periods of 2020–2050. Decision variables include gasifier temperature, steam to carbon ratio, and power generation technologies. The optimization result demonstrates that the lower steam to carbon ratio gives lower LCOE and higher CO 2eq emissions, and temperature variation gives no significant impact on LCOE and as it increases, CO 2eq emission is reduced. It demonstrates that a gas turbine is the best option for generating electricity from 2020 to 2040 and beyond 2040 SOFC is the best option.

The conversion of biomass into gasoline and diesel in bio-refinery process is an attractive process given its carbon neutral and sustainable nature. The economics of bio-refinery can be improved via integration with petroleum refinery, whereby bio-refinery intermediates can be processed into gasoline and diesel in the well-established petroleum refinery processing units, i. e., hydrocracking (HC) and fluidized catalytic cracking (FCC) units. However, the integration of the new bio-refinery into the existing petroleum refinery may not give the optimum solution given the capacities constraints of the existing petroleum refinery upgrading units such as FCC and HC units. Thus, this work proposed a superstructure comprising new bio-refinery and new petroleum refinery block diagrams. The superstructure was formulated into mixed integer nonlinear programming (MINLP) model. The model was coded into general algebraic modeling system (GAMS) platform and solved using global optimum solver, LINDOGLOBAL. The model application was demonstrated using representative case study. The model results showed that the optimum integrated bio-refinery and petroleum refinery topology favors the upgrading of bio-refinery intermediates using petroleum refinery HC unit under one-through operation mode with a marginal increase in the profit of about 0.39% compared to the second optimum case of upgrading bio-refinery intermediate using petroleum refinery FCC unit under gasoline operation mode. Thus, the decision in selecting the most suitable topology can be made based on the market demand for gasoline and diesel as the topology that uses FCC maximizes gasoline production and the topology that uses HC maximizes diesel production.

Hydrotropes have been largely explored as reactive extraction agent for lignin separation. In this paper, a mathematical model of hydrotropic-reactive extraction of sugarcane bagasse lignin was proposed and validated by experimental data from literature. The mathematical model was developed by assuming the particle is in slab shape, and by considering simultaneous processes of hydrotrope intra particle diffusion, second order reaction of lignin-hydrotrope, and intra-particle soluble delignification product diffusion. The proposed model results in a set of partial differential equations which were then solved by explicit finite difference approximation method. The mathematical model parameters were determined by fitting the model to the hydrotropic reactive extraction experimental data reported by Ansari and Gaikar (2014). Simulations show that the mathematical model of the hydrotropic-reactive extraction were well fitted to the experimental data with the obtained hydrotrope effective diffusivity ( D eA ) of 5.0 × 10 −11  m 2 /s, effective diffusivity of soluble lignin product ( D eC ) of 9.0 × 10 −12  m 2 /s and reaction rate constant ( k r ) of 1.78 × 10 −10  m 3 /(g.s). It was also observed that the reaction was first order to the hydrotrope ( n  = 1), and one half order to the lignin ( m  = 0.5). Meanwhile the pseudo-stoichiometric mass ratio of hydrotrope to lignin was 6.4 g hydrotrope/g lignin.

The highly exothermic nature of the low-density polyethylene (LDPE) polymerization process and the heating-cooling prerequisite in tubular reactor can lead to various problems particularly safety and economic. These issues complicate the monomer conversion maximization approaches. Consequently, the dynamic optimization study to obtain maximum conversion of the LDPE is carried out. A mathematical model has been developed and validated using industrial data. In the dynamic optimization study, maximum monomer conversion ( X M ) is considered as the objective function, whereas the constraint and bound consists of maximum reaction temperature and product melt flow index (MFI). The orthogonal collocation (OC) on finite elements is used to convert the original optimization problems into Nonlinear Programming (NLP) problems, which are then solved using sequential quadratic program (SQP) methods. The result shows that five interval numbers produce better optimization result compared to one and two intervals.

Natural gas is one of the main sources of energy. It contains mainly methane and less percentage of impurity compound (CO 2 , H 2 S, and N 2 ). The existence of these undesired impurity compounds in natural gas are not needed, because the presence of the acid gases in natural gas can cause corrosion and lowering the heating value in addition to their hazardous nature. The compound severely influenced human health and cause global warming. Accordingly, the capture of the acid gases species (i. e., CO 2 , H 2 S) from natural gas is essential. There are many techniques used for this purpose, hollow fiber polymeric membrane is a promising technique for this purpose. In this article, a numerical model is developed to study the effect of membrane contacting process with diverse fiber bore diameters on the percent removal of CO 2 from a gas mixture by means of aqueous MEA/water solution as a scrubbing solvent. The developed model is validated utilizing data available in literature. The verified model is used to investigate the effect of flow rate of liquid and gas, and membrane total contact area on the CO 2 removal efficiency. Results revealed that, membrane bore diameter and liquid flow rate have strong impact on the percent removal of CO 2 . The membrane with smaller bore diameter performs better than the other modules with greater diameter.