Band 17, Heft 6

In this paper, the Discrete Element Method of simulation was used to study the catalytic granule size effect on the efficiency of a bed reactor for the ethylbenzene dehydrogenation reaction. The model constructed for the laboratory experiment was made of catalyst granules of lengths 3, 6 and 9 mm, and diameters 2.8, 3, and 3.2 mm. A detailed evaluation of the catalyst total surface area and porosity effect was conducted owing to the analysis of particles size effect on the packing. Different results were observed for a wide feed gas mixture rate. Calculations performed allowed to deduce dependences of the reaction product concentration, the pressure drops, and the reactor productivity for all the particle sizes investigated.

Acetone–butanol–ethanol–water mixture is obtained by fermentation of biomass namely, corncob, wheat straw, sugarbeets, sugarcane, etc. For using the individual components, one alternative is to separate the mixture by distillation, which is costly and energy intensive operation. This paper proposes its other use in available conditions to produce hydrogen fuel by oxidative steam reforming process. For the proposed process, thermodynamic equilibrium modeling has been performed by using non-stoichiometric approach of Gibbs free energy minimization. The compositions of acetone, butanol and ethanol in mixture are 0.33:0.52:0.15 on molar basis. The influence of pressure (1–10 atm), temperature (573–1473 K), steam to ABE mixture molar feed ratio ( F ABE  = 5.5–8.5), and oxygen to ABE mixture molar feed ratio ( F OABE  = 0.25–1) have been tested by simulations on the yield of products (at equilibrium) namely, H 2 , CH 4 , CO 2 , CO, and carbon as solid. The optimum conditions for maximum production of desired H 2 , minimization of undesired CH 4 , and elimination of carbon (solid) formation are T  = 973 K, P  = 1 atm, F ABE  = 8.5, and F OABE  = 0.25. Under same operating conditions, the maximum generation of H 2 is 7.51 on molar basis with negligible carbon formation. The total energy requirement for the process (295.73 kJ/mol), the energy required/mol of hydrogen (39.37 kJ), and thermal efficiency (68.09%) of the reformer have been obtained at same operating conditions. The exergy analysis has also been investigated to measure the work potential of the energy implied in the reforming process.

A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

Gasification is one of the most efficient techniques for sustainable hydrogen production from biomass. In this study, a comparative performance analysis of the gasification process using various types of biomass materials was undertaken via thermodynamic approach. Air, steam, and air/steam as the traditional gasifying agents were applied to provide an opportunity to choose the most proper agent in the process. This paper also evaluates the environmental impacts of the process in terms of CO 2 emission by using Aspen Energy Analyzer. The effects of agent to biomass molar ratio, agent inlet temperature, moisture content of biomass material, and gasification temperature were estimated based on the producer gas compositions, hydrogen yield and heating values. The results indicate that the highest hydrogen yield (0.074 g H 2 /g biomass) was obtained in the steam gasification of plastic, while air gasification of paper generates the lowest one. It was also observed that manure is the most beneficial from environmental perspectives, while tire and plastic have the highest contribution to CO 2 emission and consequently global warming. The higher values of hydrogen production and LHV of produced gas are associated respectively with using steam, air/steam, and air as the gasification agents. The lowest value of CO 2 emission is obtained for air, air/steam, and steam as the gasifying agents, respectively.

Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Control of multi input and multi output (MIMO) process with interaction is often encountered in process industry. Such MIMO processes are controlled using conventional sliding mode controller (SMC) and tuned by integral square error (ISE) minimizing criterion based Nelder-Mead algorithm. SMC tuned by integral time absolute error (ITAE) minimization criterion based Nelder-Mead algorithm is proposed in this work. Three categories of two inputs and two outputs (TITO) process models are represented in the matrix form, with each of the matrix element representing a first order plus dead time (FOPDT) process. These TITO models are categorized based on the ratio ε , between dead time and time constant of the FOPDT model which forms the matrix element of the TITO model. The performance of conventional SMC is evaluated for these three categories of TITO models, in which the TITO process models with the ratio ε greater than the one, exhibited by poor closed loop performance, whereas the proposed SMC when applied to the these process models delivered superior closed loop performance.

The complexity of the pressure swing adsorption (PSA) mathematical model, the need for its multiple calculations to reach the cyclic steady state and a large number of functional dependencies lead to unstable numerical circuits, physically unrealistic oscillations in adsorption profiles, an increase in the calculation time, and the failure of the solver. The paper proposes an approach to optimizing the calculation process, which consists in finding a reasonable balance between the completeness of the PSA mathematical model and the accuracy of the results obtained. The effectiveness of the approach is demonstrated on the example of air oxygen enrichment and hydrogen recovery from synthesis gas. The gas separation processes were simulated for the two-adsorber PSA unit with a granulated 13X adsorbent. The effect of the changes in the model coefficients on its accuracy in the operating range of input variables is investigated. A distinctive feature of this study is the recommendations for choosing a set of the model equations to calculate the PSA processes which are particularly relevant when solving optimization problems with uncertainty. The productivity, cycle duration, the diameter of the adsorbent particles and the flow rate, at which it is advisable to use the isothermal and external diffusion reduced PSA model in the calculations, are established, which will save at least 24.3 and 47.1% of the CPU time with a small loss in accuracy. The proposed approach can be used to form a set of equations for the PSA, rPSA, ultra rPSA, VSA, VPSA models, separation of various gas mixtures on various adsorbents.

Efficient control of industrial delay processes is a challenging problem in the field of process control. Time delays are generally experienced in industrial processes from distance velocity lags, composition analysis loops, recycle time, mass, and energy transportation time. A high time delay adds a large phase lag to the system, thereby affecting the closed-loop control system stability and thus not easily controlled with PID approach. Smith predictor (SP) is a prominent technique based on process model for processes with high time delay. Unfortunately, the performance of SP deteriorates when the plant model is inaccurate. To overcome the problems related to conventional SP, various modifications have been suggested over the years in terms of structure alterations and controller parameters tuning improvements. This paper focuses on a comparative study of various Smith predictor configurations available in the literature for controlling inverse, integrating, stable and unstable industrial processes with time delay.