Chemical Vapor Deposition (CVD) is a suitable way to prepare silicon carbide (SiC) nanowires. The deposition temperature, gas velocity, and distribution of mixed gas components in the CVD tube are the primary factors affecting the final deposition rate. The influence of process parameters, consisting of deposition temperature, the flux of MTS, the flux of mixed gases and the ratio of Ar and H₂, on deposition rate of SiC nanowires were calculated numerically. The process parameters achieved optimization by an orthogonal L₉(3)⁴ test to get high deposition rate SiC nanowires. The optimal deposition rate for SiC nanowires is obtained under the following operating conditions: deposition temperature of 1473 K, flux of MTS of 5 mL/min, flux of mixed gases of 525 mL/min, and Ar : H₂=1 : 1. Two calculation results were verified by the corresponding experimental results, which revealed the accurateness of the simulation results remarkably.

To recovery Pd particles in viscous acetone solution including nitrile butadiene rubber, the solution of water-soluble polymer, poly(styrene-2-(dimethylamino)ethyl methacrylate) (poly(styrene-DMAEMA)) were added, understanding especially the effect of copolymerization of styrene as well as fluid behavior in aim of applying to scale-up reactor. The added polymer was dehydrated, after which a poly(styrene-DMAEMA) precipitate containing Pd particles was obtained. By increasing the copolymerization percentage of styrene, the recovery percentage of the Pd particle varied dynamically from 60 to 80% due to the high affinity of the polymer to the Pd particle, high dehydration speed, dissolution speed of poly(styrene-DMAEMA), and size of the precipitate. In the scale-up batch mode, prolonged stirring corresponds to a high recovery percentage and mass transfer coefficient of the Pd particle. The optimum copolymerization percentage of styrene required to achieve the high recovery percentage and mass transfer coefficient in scale-up batch mode was determined to be 9%.

Excessive discharge of phosphorus (P) from wastewater into water bodies can lead to water eutrophication and deleteriously affects aquatic lives and the surrounding ecosystem. Hence, phosphorus removal from wastewater is necessary for the control of eutrophication of the receiving water bodies. This study investigated the removal of phosphate using calcium silicate hydrate (CSH) synthesized from Tonle Sap Lake sediment and bivalve shell, a kind of silica and calcium-rich abundant natural waste materials, respectively. The CSH was synthesized through a hydrothermal method at a temperature of 110°C for 12 h with varying Ca/Si molar ratio from 0.83 to 1.5. The study on utilization of CSH for phosphate removal was done by batch experiments. The effect of Ca/Si molar ratio, contact time and initial pH on phosphate adsorption capacity was investigated. The result showed that the Ca/Si molar ratio of 1 is the optimum synthesis condition for phosphate removal. Based on Si released and FTIR results, the phosphate adsorption process occurred by ion exchange with silicate. The equilibrium data of phosphate adsorption fitted well with the Langmuir model and the maximum phosphate removal capacity onto CSH was 270.27 mg/g.

A combined process that recycles scrap tire rubber and degrades dye wastewater simultaneously using sub/supercritical water was studied. The effect of the reaction temperature and time on the treatment process was investigated. The apparent reaction kinetics was used to analyze the effect of dyes on the recycling of scrap rubber. The experimental results show that both the degree of devulcanization of the scrap rubber and the degradation rate of the wastewater are proportional to the reaction temperature and time. When the reaction temperature is 360°C and the reaction time is 3 h, the sol fraction in devulcanized rubber exceeds 30%, and the chemical oxygen demand (COD) removal rates of both dye wastewaters exceed 90%. Regardless, when the reaction temperature reaches 380°C, the rubber suffers from excessive devulcanization, which in turn increases the COD of wastewaters. Therefore, the key to create a successful combined process is regulating the reaction temperature. The results of apparent reaction kinetics analysis show that the dye wastewater can inhibit the devulcanization process, and the inhibition of disperse dye wastewater is stronger than that of acid dye wastewater, resulting in a lower reaction rate of devulcanization.

Autothermal dry reforming of methane (ATR-DRM) has gained considerable research attention for its application in the conversion of methane and carbon dioxide to valuable synthesis gas (syngas). However, granular Ni-based catalysts offer limited stability and durability when used in the dry reforming of methane (DRM). Here, the performance of a honeycomb-type Ni/Al₂O₃ structured catalyst during syngas production was investigated under ATR-DRM conditions. The catalyst was successfully prepared on an aluminum-fin substrate by a combination of sol–gel and electroless plating methods. The addition of oxygen during the DRM significantly enhanced the reforming performance and considerably suppressed coke deposition. Furthermore, the Ni/Al₂O₃ structured catalyst could reduce hot spot formation and improve energy efficiency. Therefore, ATR-DRM using a structured catalyst is a promising technology that promotes process intensification.

Compared with principal component analysis (PCA)-based methodologies, independent component analysis (ICA) is more suitable for extracting the variation information of latent variables. The advanced fault detection approach based on ICA utilizes two statistics, I²_d and square prediction error (SPE), to monitor the health status of a single mode process. However, when these statistics are used for monitoring a multimode process, their fault detection performances are disappointed. Aiming to detect some faults caused by abnormal changes of latent variables in a multimode process, a novel health status monitoring scheme based on difference in independent component (Diff-ICA) is proposed in this paper. In Diff-ICA, the conventional ICA is firstly implemented to extract the latent information of a process. Next, the estimated independent components of each sample are calculated using k nearest neighbors (kNN) rule. After that, a new statistic I²_diff, which is built based on the difference between real independent components and estimated independent components, is used for monitoring the variation of latent variables in this process. Meanwhile, another statistic q_diff, which is built in the residual subspace that is reconstructed using these estimated independent components, is applied to monitor the health status of this process. Diff-ICA is capable of both reducing the impact of multimode characteristics on process monitoring and improving fault detection rate of a multimode process. The efficiency of Diff-ICA is demonstrated by a simulated case as well as an industrial case. The experimental results indicate that the proposed method outperforms PCA, kernel PCA (KPCA), ICA and FD-kNN.

Most classical fault diagnosis methods such as principal component analysis (PCA), are extracting comprehensive information to represent data features in fault diagnosis. In comparison, Non-Negative Matrix Factorization (NMF) is a method for dimension reduction and feature extraction, and because its characteristic matrix has sparsity, this method is superior in the ability of extracting the local feature and suppressing noises; however, the NMF method is not applicable for dynamic industrial processes. In this paper, we introduce the past information of industrial processes for fault diagnosis, proposing Canonical Variate Analysis - Non-Negative Matrix Factorization (CVA-NMF) methods to improve the dynamic performance of NMF. The experimental results via TE process indicate that the proposed approach could handle a dynamic production process such as Fault 2 and Fault 5 and retain the superior performance of NMF.

Principal component analysis (PCA) has been widely applied in chemometrics and process monitoring. Because the principal component (PC) is a combination of all original variables, its interpretation is often not straightforward. Recently, sparse PCA methods have been developed to generate sparse loading vectors. The obtained sparse principal component (SPC) is much easier to interpret. However, the sparser loading vectors and the lower variance are achieved by SPCs. The sparsity-variance trade-off is usually represented by the index of sparseness which is determined by the number of non-zero loadings on each SPC. In this paper, we propose a novel method for the selection of NNZL using particle swarm optimization (PSO) for sparse PCA. The proposed method is applied to process monitoring. Two case studies are used to verify the capability and efficiency of the proposed method.

Process monitoring is of importance to maintain process safety, reliability, performance and cost efficiency. This work presents a hybrid fault detection approach that combines process knowledge such as first-principles and process causal relations into data-driven fault detection techniques. The process knowledge is embedded into the process dataset as the form of extended attributes (ExAs). In this paper, we discuss the benefits of adding process knowledge into the process data, as well as the procedure of extracting ExAs from available process information such as piping and instrument digraph. Our proposed method was successfully tested on the Tennessee Eastman Process using two commonly utilized data-driven fault detection techniques: principle component analysis and its variant kernel PCA.

A process for selectively recovering titanium, vanadium, and tungsten from spent denitrification catalysts generated in coal-fired power plants was developed in this study. Carbon was added as a reducing agent and the chloride volatilization behavior of rare metals under a chlorine gas flow was subsequently tracked. This demonstrated that the chloride volatilization reaction is promoted by the addition of carbon. In addition, if the spent catalyst is heat-treated in methanol vapor to deposit solid carbon on the catalyst surface, the chloride volatilization promotion is particularly high. After carbon was added by this method, heating to 400°C under a chlorine gas flow selectively released rare metals without releasing coexisting elements such as iron and aluminum. In addition, the released rare metals were converted into chlorides upon cooling and were completely recovered in solid or liquid form.

We analyzed mercury forms in desulfurization gypsum generated at a coal-fired power plant via temperature-programmed desorption (TPD) and cold vapor atomic absorption. The gypsum used as samples was obtained from double contact flow and jet bubbling desulfurization equipment. There were three types of gypsum samples for each type of desulfurization equipment, and six types of samples in all. The total mercury in these samples was 0.36–1.85 ppm. For model samples, we prepared and analyzed carbon with mercury that was adsorbed onto unburned carbon particles separated from fly ash or onto bituminous coal-based activated carbon. The desorption temperature varied depending on the carbon species; as the amount of adsorption increased, the peak width also increased. The TPD behavior of desulfurization gypsum was simulated with a linear combination of TPD curves from model samples. Mercury was found in carbon contained in all desulfurization gypsum analyzed in the present study. Mercury in carbon in the desulfurization gypsum obtained from the double contact flow desulfurization equipment could be simulated with model samples for unburned carbon with adsorbed mercury alone. Further, desulfurization gypsum obtained from jet bubbling desulfurization equipment could be simulated using unburned carbon with adsorbed mercury and activated carbon with adsorbed mercury. With the jet bubbling type of equipment, the abundance ratios of HgO, HgS, and HgSO₄·2HgO were higher than those in double contact flow equipment, showing that depending on the type of desulfurization equipment, not only the type of mercury in carbon but also mercury forms in gypsum vary notably.

Open-cell porous silicon is gaining attention for use as a negative electrode to increase the theoretical capacity of lithium-ion batteries. Open-cell porous silicon is prepared by eluting Mg from Mg–Si alloy in molten Bi. To reuse the expensive Bi, separation of trace Mg from Bi is indispensable. Although metallic Mg and Bi form a uniform melting state, MgCl₂ floats on the Bi melt. To separate Mg from Mg–Si alloy, effects of different types of chlorinating agents, reaction temperatures, and reaction times on chlorination behavior of Mg and Bi were investigated in this study. When the alloy was heated at 600°C for 21 h, as accumulated reaction time under the decomposition of NH₄Cl gas, the Mg content in the sample decreased from the initial 0.9 to 0.1% and 90% of Bi was recovered as melt.