Cu/Ni‐Loaded CeO2‐ZrO2 Catalyst for the Water‐Gas Shift Reaction: Effects of Loaded Metals and CeO2 Addition

Two different types of metals (Cu and Ni) and the effect of CeO₂ addition to produce a CeO₂‐ZrO₂ co‐supporter were investigated through the water‐gas shift (WGS) reaction. It was found that the WGS activity could be enhanced with CeO₂ addition. At relatively high temperature, Ni‐loaded catalysts exhibited higher CO conversion while Cu‐loaded catalysts demonstrated better performance at low temperatures. The stability and yield of the CO₂ and H₂ products of the Cu catalysts were higher than those of the Ni catalysts. These results may be caused by an irreversible adsorption of CO on Ni and the reverse WGS reaction occurring on the Ni catalysts. In situ diffuse‐reflection infrared Fourier transform spectroscopy data suggests that the WGS mechanism likely proceeded via formate species.     

Homogeneous polymer/filler composite membrane by spraying method for enhanced direct methanol fuel cell performance

In this work, composite membranes for a direct methanol fuel cell (DMFC) were prepared using a spraying method to improve cell performance especially at a high methanol concentration. Nafion polymer and mordenite as a filler were used for the composite membrane preparation using a spraying method and a conventional solution casting method and the membranes from the two methods were compared. SEM images showed that a more homogeneous composite membrane could be obtained using the spraying method. The effect of mordenite content was also studied. The membranes were consequently characterized and tested in DMFC operation. The results were compared to those prepared using the solution casting method at 30, 50, and 70 °C with methanol concentrations of 2, 4, and 8 M. It was found that the membrane with 5 wt.% mordenite from the spraying method showed a vast improvement in DMFC performance. When the cell was operated at 70 °C, the maximum power density of 5 wt.% mordenite from the spraying method was higher than that of commercial membrane and 5 wt.% from the solution casting method. Power densities from the 5 wt.% sprayed membrane were higher by around 29%, 40%, and 60% at 2, 4, and 8 M methanol concentration, respectively.     

Co‐Metal Catalysts on SiO2‐TiO2 for Methanol Production from CO2 – Effect of Preparation Methods

The effect of quantity, composition, and different impregnation sequences on the catalytic properties of Cu‐Zn‐Al/SiO₂‐TiO₂ in the CO₂ hydrogenation for methanol production was investigated. The Cu‐Zn‐Al catalysts supported on SiO₂ and TiO₂ were prepared by incipient wetness impregnation. Then, their performances in CO₂ hydrogenation were tested under defined conditions. The composition variation of Cu and Zn catalysts resulted in a high methanol production for Cu catalysts with a higher content of Cu, which was the active site for CO₂ activation. Regarding the metal quantity of catalysts, a relatively low loading of co‐metal (Cu‐Zn‐Al) led to the maximum methanol yield when compared with higher loadings as a result of the largest surface area.     

Toughening of epoxy hybrid nanocomposites modified with silica nanoparticles and epoxidized natural rubber

Silica nanoparticles (SN) and epoxidized natural rubber (ENR) were used as binary component fillers in toughening diglycidyl ether of bisphenol A (DGEBA) cured cycloaliphatic polyamine. For a single component filler system, the addition of ENR resulted in significantly improved fracture toughness (K_IC) but reduction of glass transition temperature (T_g) and modulus of epoxy resins. On the other hand, the addition of SN resulted in a modest increase in toughness and T_g but significant improvement in modulus. Combining and balancing both fillers in hybrid ENR/SN/epoxy systems exhibited improvements in the Young’s modulus and T_g, and most importantly the K_IC, which can be explained by synergistic impact from the inherent characteristics associated with each filler. The highest K_IC was achieved with addition of small amounts of SN (5 wt.%) to the epoxy containing 5–7.5 wt.% ENR, where the K_IC was distinctly higher than with the epoxy containing ENR alone at the same total filler content. Evidence through scanning electron microscopy (SEM) and transmission optical microscopy (TOM) revealed that cavitation of rubber particles with matrix shear yielding and particle debonding with subsequent void growth of silica nanoparticles were the main toughening mechanisms for the toughness improvements for epoxy. The fracture toughness enhancement for hybrid nanocomposites involved an increase in damage zone size in epoxy matrix due to the presence of ENR and SN, which led to dissipating more energy near the crack-tip region.     

Investigating flow patterns in a channel with complex obstacles using the lattice Boltzmann method

In this work, mesoscopic modeling via a computational lattice Boltzmann method (LBM) is used to investigate the flow pattern phenomena and the physical properties of the flow field around one and two square obstacles inside a two-dimensional channel with a fixed blockage ratio, β=1/4, centered inside a 2D channel, for a range of Reynolds numbers (Re) from 1 to 300. The simulation results show that flow patterns can initially exhibit laminar flow at low Re and then make a transition to periodic, unsteady, and, finally, turbulent flow as the Re get higher. Streamlines and velocity profiles and a vortex shedding pattern are observed. The Strouhal numbers are calculated to characterize the shedding frequency and flow dynamics. The effect of the layouts or configurations of the obstacles are also investigated, and the possible connection between the mixing process and the appropriate design of a chemical mixing system is discussed.     

Product quality improvement of batch crystallizers by a batch-to-batch optimization and nonlinear control approach

Batch crystallization is one of the widely used processes for separation and purification in many chemical industries. Dynamic optimization of such a process has recently shown the improvement of final product quality in term of a crystal size distribution (CSD) by determining an optimal operating policy. However, under the presence of unknown or uncertain model parameters, the desired product quality may not be achieved when the calculated optimal control profile is implemented. In this study, a batch-to-batch optimization strategy is proposed for the estimation of uncertain kinetic parameters in the batch crystallization process, choosing the seeded batch crystallizer of potassium sulfate as a case study. The information of the CSD obtained at the end of batch run is employed in such an optimization-based estimation. The updated kinetic parameters are used to modify an optimal operating temperature policy of a crystallizer for a subsequent operation. This optimal temperature policy is then employed as new reference for a temperature controller which is based on a generic model control algorithm to control the crystallizer in a new batch run.     

间歇结晶过程的分批优化

It is the fact that several process parameters are either unknown or uncertain. Therefore, an optimal control, profile calculated with developed process models with respect to such process parameters may not give an optimal performance when implemented to real processes. This study proposes a batch-to-batch optimization strategy for the estimation of uncertain kinetic.par.ameters in a batch crystallization process of potassium sulfate production. The knowledge of a crystal size distribution of the product at the end of batch operation is used in the proposed methodology. The updated kinetic parameters are applied for determining an optimal operating temperature policy for the next batch run.     

Model predictive control for the reactant concentration control of a reactor

The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors.     

Dynamic neural network modeling for hydrochloric acid recovery process

This paper describes neural network models for the prediction of the concentration profile of a hydrochloric acid recovery process consisting of double fixed-bed ion exchange columns. The process is used to remove the Fe²⁺ and Fe³⁺ ion from the pickling liquor, resulting in increasing the acid concentration for reusing in the pickling process. Due to the complexity and highly nonlinearity of the process, the modeling of the process based on the first principle is difficult and involve too many unknown parameters. Therefore, an attractive alternative technique, neural network modeling, has been applied to model this system because of its ability to model a complex nonlinear process, even when process understanding is limited. The process data sets are gathered from a real hydrochloric acid recovery pilot plant and used for neural network training and validation. Backpropagation and Lenvenberg-Marquardt techniques are used to train various neural network architectures, and the accuracy of the obtained models have been examined by using test data set. The optimal neural network architectures of this process can be determined by MSE minimization technique. The simulation results have shown that multilayer feedforward neural network models with two hidden layers provide sufficiently accurate prediction of the concentration profile of the process.     

Neural network based model predictive control for a steel pickling process

A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input–output data sets obtaining from mathematical model simulation. The Levenberg–Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases.