Volume 9, Issue 2 -

The kinetic mechanism of the Fischer–Tropsch synthesis (FTS) is complex resembling a polymerization reaction. The kinetic rate constants for initiation, propagation and termination steps and the constants for the equilibrium reactions for methylene formation ( in situ monomer) need to be estimated. A mathematical model for the FTS allows for simulating several operating conditions and determining the best operating conditions to produce a specific product distribution, so the kinetic parameters must be statistically valid. This work used neural networks (NNs) to estimate the FTS kinetic parameters, instead of using methods based on least squared error. The results show that NNs with three hidden layers were able to output good estimates of the kinetic parameters with less than 5% of deviation.

The role of temperature is important in CO 2 capture processes. Unfortunately, detailed analysis on the temperature profile of the absorption column is scarce in the literature. Important factors like CO 2 capture capacity and corrosion rate directly depend on temperature of the column. Many side reactions such as solvent degradation, formation of stable salts, corrosion and reduction in CO 2 capture are prominent at a higher temperature. This study reports a broad study on the temperature profile for CO 2 capture process based on a detailed mathematical model, Kent–Eisenberg vapor–liquid equilibrium (VLE) model. This model is quite accurate in calculating CO 2 capture for any specific operating condition. Results produced from Kent–Eisenberg VLE model are consistent with experimental data. This study reports temperature profiles of an absorption column for different operating conditions. Moreover, it was found that CO 2 absorption is more effective at low and ambient temperatures than at high temperature confirmed by a peak temperature in all cases and in the lower section of the column, which is attributed to exothermic CO 2 absorption in monoethanolamine. This temperature variation of the column will be helpful in designing CO 2 capture plants.

In this study, the feasibility of glycerol valorization into homo- and hetero-polymers of polyhydroxyalkanoates by a sludge isolated Bacillus sp. RER002 in a 3 L bioreactor was investigated. A mathematical model including logistic, Luedeking–Piret, and Luedeking–Piret-like equations that simulated the active residual biomass growth, P(3HB) synthesis, and glycerol consumption, respectively, was developed. In order to describe the dynamics of batch P(3HB) production, the model kinetic parameters viz., µ max , K 1 , K 2 , α , β , and K N were optimized using the stochastic search-based genetic algorithm. The synthesis of P(3HB) wasobserved to be highly growth associated and partially non-growth associated as reflected in a significant higher values of K 1 (0.2435–0.5477) than K 2 (2.2 × 10 −6 to 9.1 × 10 −3 ) within the glycerol concentration range of 10–40 g/L. Besides, the maximum 3.2g/L of copolymer [P(3HA scl -co-3HA mcl )] was observed at 30 g/L of glycerol concentration in synthetic crude glycerol medium with a yield coefficient ( Y P / S ) of 0.16 g/g. Furthermore, the analyses of chemical and thermal properties of copolymer P(3HA scl -co-3HA mcl ) revealed its enhanced material properties which make it suitable for various applications.

Coals are invariably associated with mineral matter, which makes it unsuitable for efficient utilisation. For difficult-to-wash coals, advanced coal beneficiation technologies like chemical leaching methods are under development. In this paper, kinetic equations using different methods have been evolved, and related parameters have been estimated, using the experimental results obtained during coal leaching process. As coal is a heterogeneous rock, three different methods namely (i) parametric estimation through rate equation, (ii) non-linear regression and (iii) parametric estimation through shrinking core model have been developed and validated to check the minimum level of permitted error tolerance. Experiments were designed, using full factorial design with three variables, which are sensitive to the process. Values of activation energy and k 0 obtained, using the parametric estimation of rate equation and shrinking core model, are almost in the same range. The order of the reaction for silica and alumina is two, using rate equation method. The parametric data obtained from the polynomial regression method were compared with the actual data. The exponential polynomial provides a better fit for the chemical leaching process of coal.

A kinetic scheme of radical chain reactions in autocatalytic pyrolysis of ethane was studied using a sensitivity analysis method, bringing in the experimental data. In the gas-phase kinetic experiments, ethane pyrolysis was carried out in laboratory reactors with the reaction mixture heated by CO 2 laser irradiation. It was shown that the scheme with autocatalytic routes includes as few steps as possible and adequately describes the ethane pyrolysis with high ethylene yield at 900–1,150 K. Admissible variation ranges of preexponential factors and activation energies for the kinetic model of the reactions were found using the Monte Carlo statistical method. Reducibility of the scheme was examined by means of the Sobol’s variance based strategy applied for the sensitivity analysis evaluation.

In this study, Ni–Y and Ce–Y zeolites are prepared using synthesized Na–Y zeolite through solid-state ion-exchange method. The adsorptive desulfurization of a model gasoline containing 194, 116 and 72 ppmw sulfur is evaluated in a batch system under ambient conditions. A dynamic model is established in order to investigate the performance of the adsorption process. The model predictions are compared with the obtained experimental results for thiophene adsorption on Ni–Y and Ce–Y zeolites from model solution containing different concentrations of thiophene, and a good agreement is observed. The model parameters: diffusivity and mass transfer coefficient are estimated by comparing the model predictions and experimental data.

Decentralized PID control has been extensively used in process industry due to its functional simplicity. But designing an effective decentralized PID control system is very challenging because of process interactions and dead times, which often impose limitations on control performance. In practice, to alleviate the detrimental effect of process interactions on control performance, decoupling controllers are often incorporated into a decentralized control scheme. In many cases, these conventional decoupling controllers are not physically realizable or too complex for practical implementation. In this paper, we propose an alternative scheme to overcome the performance limitation imposed by process interactions. This new control scheme is extended from the SISO multi-scale control scheme previously developed for nonminimum-phase processes. The salient feature of the new control scheme lies in its communicative structure enabling collaborative communication among all the sub-controllers in the system. This communicative structure serves the purpose of reducing the detrimental effect of process interactions leading to improved control performance and performance robustness. Extensive numerical study shows that the new control scheme is able to outperform some existing decentralized control schemes augmented with traditional decoupling controllers.

Bioethanol has been found to be a suitable substitute for gasoline in internal combustion engines. It could be used either in an undiluted form or blended with gasoline. To blend the ethanol and gasoline, the water content of ethanol should reach 0.5% or less. In the present research work, 3A Zeolite was used as an absorbent with vacuum distillation. The effects of the operating parameters such as temperature, vacuum pressure and vapor flow rate on ethanol – water separation were investigated. Final ethanol concentration was obtained at the end of every run as well as the concentration of outlet ethanol. Both linear regression and ANN design were used to determine the best fit for two final parameters. The optimum condition was obtained at 0.4 bar vacuum pressure and 20 l/min ethanol – water vapor flow rate. ANN model is more qualified to the simulation of outspread data while the linear regression is not. L10L10 mode and L5T10 mode provide the best results for final concentration and total time, respectively. The Trainlm Algorithm like the previous research training algorithm is the best.