In this work, the fluidization of cohesive particles in a bubbling fluidized bed was simulated using a modified kinetic theory of the granular flow-modified population balance model (MKTGF-MPBM) coupling method. In the MKTGF, the restitution coefficient of the collision was corrected using the square well potential, and in view of the particle collision deformation, the particle temperature θ_e was modified. In the MPBM, the collision efficiency α was introduced to correct the turbulence agglomeration kernel and the mean square root was used to modify the total agglomeration kernel function. The simulation results showed that the viscosity correlation coefficient, obtained after the modification of collision restitution coefficient was μ_s=0.018ε_s^1/3 g₀, and the average value of the modified particle temperature (pulsating energy) was 0.002–0.0045 m²/s². The corrected results are more consistent with the experimental data.

The potential applications of forward osmosis (FO) for arsenic (As) removal from water requires an improved understanding of As rejection performance. This study investigated the effects of As concentration and pH in the feed solution, the salt concentration in the draw solution, and surface orientation of a FO membrane on As rejection. The results showed that the pH of the solution played an important role in As rejection. Salt concentration in the draw solution significantly influenced the water flux through the membrane. The concentration polarization with respect to surface orientation was a key factor for As retention; FO operation using the membrane with the active layer oriented toward the feed solution (AL-FS) was more effective than using the membrane with the active layer oriented toward the draw solution (AL-DS).

The effects of Brownian motion and turbulent motion on particle agglomeration in turbulent agglomerator were evaluated. Particle agglomeration was numerically simulated by establishing a KTGF (Kinetic Theory of Granular Flow)-RSM (Reynolds Stress mathematical Model)-PBM (Population Balance Model). Capture efficiency α and fractal dimension were introduced to modify the turbulent agglomeration kernel model. The RMS (Root Mean Square) method used to obtain a kernel model suitable for turbulent agglomeration of coal-fired PM2.5. The model was compared with the traditional turbulent agglomeration model. The results show that the dissipation rate in the vortex area clearly increased, and the particle motion became aggregative fluidization to enhance the agglomeration effect. When the fractal dimension is 2.5, it is suitable for the turbulent agglomeration of nonuniform structure, and the Brownian motion in the calculation of kernel function cannot be ignored. The RMS model is closer to reality. Compared with the experimental results, the maximum error of ideal turbulent agglomeration model is ∼8%, whereas the maximum error of new turbulent agglomeration model is only 3%.

The present study investigates the deacidification of papers with hexamethylenetetramine (HMT) in alcohols and supercritical carbon dioxide. The effects of various treatments, including HMT-IM (treated by immersion), HMT-SC (treated by supercritical carbon dioxide) and artificial accelerated aging, on the pH and mechanical properties of the papers were studied. Results showed that deacidification provides a sufficient alkaline reserve in the order of HMT-IM>HMT-SC (SCCO₂)>HMT-SC (aged) to protect papers against further acid attack. Surface pH values were also apparently enhanced to a reasonable range (pH=6.7–7.0) after treatment. Deacidification significantly improved the tensile strength, folding endurance index (double folds number) and tear index of papers. In particular, SEM results proved that the tensile strength and folding endurance of papers treated by HMT-SC were better than those treated by HMT-IM. Even after accelerated aging, the surface pH and mechanical properties of the papers remained within proper ranges, thereby benefitting the long-term preservation of aged papers. EDS analysis of elemental N further proved that an HMT reserve in papers is helpful for their long-term preservation. In other words, HMT is a favorable organic deacidification agent in alcohols and supercritical carbon dioxide.

Macro-porous ceria was synthesized by the hard template (nanocasting) method using silica nanospheres as template material. Also, this ceria photocatalyst was used to remove toxic lead ions and methylene blue dye from aqueous solutions by an adsorption and UV-photocatalysis system in sequence. The macro-porous ceria had relatively high BET specific surface area and wide pore size distributions. The macro-pores consisted of spatial gaps generated by aggregates of ceria nanoparticles growing in a branch shape due to the inhibition of crystal growth with the silica nanospheres. X-ray photoelectron spectroscopy (XPS) revealed that the high adsorption performance of the macro-porous ceria is due to the presence of large amounts of surface hydroxyl groups in water. This macro-porous ceria material exhibits higher overall removal performance by the adsorption and UV-photocatalysis than conventional non-porous ceria.

Constructing an efficient and stable fault diagnosis method is crucial for industrial production. Most classical methods are inadequate in process diagnosis, as their assumptions are difficult to satisfy in real production processes. For instance, a Gaussian distribution is required for principal component analysis (PCA), canonical analysis (CVA) is only compatible with linear industrial processes, and independent component analysis (ICA) is not useful for dynamic processes. In this paper, we have proposed a novel method, Canonical Variate and Kernel Independent Component Analysis (CV–KICA), which combines the advantages of CVA and KICA, and tested this method with the Tennessee Eastman (TE) process. We first use ICA to suppress the impacts of noises in industrial production, and introduce kernel to ICA for adapting to non-linearity, then integrate the resulting components into the CVA method for dynamic processes. Simulations and experimental results with the TE process indicate that the CV–KICA method outperform other classical diagnosis methods such as CVA, KICA and DKICA in fault diagnose, providing a novel approach that could handle dynamic and nonlinear situations in real production processes.