Preparing sodium titanates from sodium carbonate and anatase by the solid sintering reaction is considered as the most effective method in manufacture. However, their synthetic conditions (reactant ratio, temperature and pressure) are very complex and the preparing process relies too much on empirical evidence. Therefore, it is important to simulate the reaction accurately and find the key factors of the synthetic conditions. In this study, the thermo-chemical data of sodium titanates preparation are estimated, and then the thermo-chemical equilibrium for the solid sintering reaction between sodium carbonate and anatase is calculated through the Gibbs energy minimization method. It can be observed that the calculated results are in accordance with the experimental ones. Later, the respective effects of reactant ratio, temperature and pressure on the products are analyzed. The results show that the effect of the pressure on all the sodium titanate products can be negligible. Moreover, the contour lines for the yield and purity of a single product have been drawn, which show that both reactant ratio and temperature are key factors in the preparation of Na2Ti6O13, and temperature is the key factor of Na2Ti3O7, while in the preparation of Na8Ti5O14, reactant ratio is the key factor.
An innovative wet scrubber system for micron and submicron particles removal from exhaust gas through a gas–liquid cross-flow array (GLCA) is proposed, which is formed by wastewater vertically falling down along a number of staggered wires. The smoothly flowing circular wastewater films on the outer surface of wires act as wet-wall columns to collect particles suspended in the exhaust gas flowing perpendicularly across it. An analytical model is developed to predict the particle removal performance of the GLCA, based on limiting trajectory analysis (LTA) of particle movement orientated by forces on a particle including diffusiophoresis (DP) and thermophoresis (TP). The LTA model, which combines particle motion equations and flowing gas velocity/temperature/humidity distributions in the boundary layer around a column, indicates that the particle grade removal efficiency (PGRE) for micron and submicron particles (0.1–10 µm) in the GLCA is dominated by different mechanisms according to particle size. DP has a more important effect causing the removal for the particle size range from 0.1–4 µm, while DP and inertia impaction dominate the removal process for the particle size range from 4–10 µm. The experiments on a lab-scale GLCA test rig equipped with Palas welas Digital 2000 were carried out with variable inlet gas temperatures and relative humidities, and a constant falling water temperature of 20°C. The PGRE measurements for the particles diameter size range between 0.1 and 10 µm compare satisfactorily with the model predictions.
The effects of heat treatment temperature and additives on the desulfurization of high-sulfur petroleum cokes were investigated in this study. Results showed that alkali (Li, Na and K) carbonate catalytic activity followed the order K>Na>Li at the same heat treatment temperature. The desulfurization chemical activation mechanism of petroleum cokes with additives was then explored using XRD techniques, and the results showed that different additives have different mechanisms and desulfurization rates corresponding to different temperatures, mainly in the form of sulfur dioxide escaping from petroleum coke particles. Petroleum coke desulfurization is controlled primarily by breakage of C–S bonds in the coke, which can be reduced by the additives as they enhance sulfur removal. Recombining C–C bonds via C–S bond breakage also creates more ordered crystal structure in petroleum cokes.
Chemical-looping coal combustion (CLC) is a high efficiency technology for the conversion of coal and the potential capture of CO2 capture. The CLC is based on the transfer of oxygen from air to the fuel by a solid oxygen carrier. This study, investigated ilmenite reduction contained two steps of Fe2O3 to FeO, and FeO to Fe by CO and the conditions that avoid the deep reduction with multiple cycles by using TG. Reaction models for the reduction of ilmenite with multiple cycles were examined. A synthetic Fe2O3/Al2O3 oxygen carrier was also tested for comparison. Ilmenite re-oxidation by air after a second reduction step caused particle agglomeration, since Fe oxidation produces large amount of heat. To protect against the deep reduction of FeO to Fe in ilmenite, short reduction times and the incomplete reduction of Fe2O3 to FeO can be used. The reduction of iron oxide in the reduction of Fe2O3 to FeO step can also be limited when 0.3< CO/(CO+CO2) <0.7. Reaction models for the reduction of ilmenite with multiple cycles were examined.
The nonlinearity and complexity of a boiler–turbine unit make its precise modeling a challenging task. In this paper, a hybrid of the two-phase feature selection and deep belief network (DBN) approach is proposed to predict the key variables of the boiler–turbine unit (e.g., steam pressure, steam flow, and active power). Actual operational data were collected from a local thermal power plant and were standardized to eliminate the effect of units with different variables. Moreover, data processing for normalization was carried out by Box–Cox transformation. Subsequently, a two-phase feature selection strategy was implemented. Using this strategy, variable selection based on Pearson correlation analysis was first carried out. Then, the PCA reconstructed the new features of the prediction model. Finally, a multilayer DBN with a back-propagation-based fine-tuning algorithm was developed to model the nonlinear relationship between the reconstructed input variables and key output variables. To validate the effectiveness of the proposed approach, numerical experiments were conducted based on practical data. The experimental results confirmed the outstanding performance of the proposed modeling approach in comparison to other classical approaches.
There exists nonlinear information and strong correlation among variables in modern industrial processes. As a typical linear process monitoring method, probabilistic principal component analysis (PPCA) cannot capture the nonlinear information among process data. To cope with this problem and improve real time performance, a new just-in-time-learning based PPCA (JITL–PPCA) method is proposed in this paper. In JITL–PPCA, an online local model structure is first designed for extracting nonlinear features, by incorporating an improved JITL approach and least squares support vector regression (LSSVR) model. Then, the remaining linear residuals are input into the PPCA scheme for final process monitoring and fault detection. A simulated numerical case and a real industrial process case are used to evaluate the performance and effectiveness of the proposed method. The monitoring results show the effectiveness of the proposed JITL–PPCA method.
This study set out to examine the use of heat integration to minimize the energy consumption of a hybrid membrane separation (vapor permeation)–distillation hybrid process for the dehydration of isopropyl alcohol (IPA). The retentate stream from the membrane was used as a heat source. Energy consumption of the hybrid process was evaluated by a model-based simulation, which was developed using Pro/II and an Excel VBA program. In the development of the simulation model, the stage-cut and reboiler duty were used as the parameters of the hybrid process. Energy consumption of the hybrid membrane separation–distillation process, which involves heat integration with the retentate stream and the reboiler, became smaller than that of a hybrid membrane separation–distillation process without any heat integration. To increase the degree of heat exchange by increasing the temperature differential, we evaluated the heat integration combinations of retentate stream and the stages in the distillation column. The distillation column consisted of nine stages, including the reboiler. Within the scope of the present study, it was found that heat integration with the fifth stage of the distillation column minimized the reboiler duty. The reboiler duty of the hybrid membrane separation–distillation process, with heat integration with the fifth stage, was 11% less than that of a hybrid membrane separation–distillation process with heat integration with the reboiler, and 48% less than that of a process without any heat integration.