Editors Ryuichi Egashira (Tokyo Institute of Technology) Jun Fukai (Kyushu University) Choji Fukuhara (Shizuoka University) Toshitaka Funazukuri (Chuo University) Takayuki Hirai (Osaka University) Jun-ichi Horiuchi (Kitami Institute of Technology) Eiji Iritani (Nagoya University) Yoshinori Itaya (Gifu University) Noriho Kamiya (Kyushu University) In-Beum Lee (Pohang University of Science and Technology (POSTEC)) Kouji Maeda (University of Hyogo) Hideyuki Matsumoto (Tokyo Institute of Technology) Nobuyoshi Nakagawa (Gunma University) Masaru Noda (Fukuoka University) Hiroyasu Ogino (Osaka Prefecture University) Mitsuhiro Ohta (The University of Tokushima) Eika (W. Qian Tokyo University of Agriculture and Technology) Yuji Sakai (Kogakuin University) Noriaki Sano (Kyoto University) Naomi Shibasaki-Kitakawa (Tohoku University) Ken-Ichiro Sotowa (The University of Tokushima) Hiroshi Suzuki (Kobe University) Nobuhide Takahashi (Shinshu University) Shigeki Takishima (Hiroshima University) Yoshifumi Tsuge (Kyushu University) Tomoya Tsuji (Nihon University) Da-Ming Wang (National Taiwan University) Takuji Yamamoto (National Institute of Advanced Industrial Science and Technology (AIST)) Yoshiyuki Yamashita (Tokyo University of Agriculture and Technology) Miki Yoshimune (National Institute of Advanced Industrial Science and Technology (AIST))
Editorial office: The Society of Chemical Engineers, Japan Kyoritsu Building, 4-6-19, Kohinata, Bunkyo-ku Tokyo 112-0006, Japan firstname.lastname@example.org
AIMS AND SCOPE:
Journal of Chemical Engineering of Japan, an official publication of the Society of Chemical Engineers, Japan, is dedicated to providing timely original research results in the broad field of chemical engineering ranging from fundamental principles to practical applications. Subject areas of this journal are listed below. Research works presented in the journal are considered to have significant and lasting value in chemical engineering.
Physical Properties and Physical Chemistry Transport Phenomena and Fluid Engineering Particle Engineering Separation Engineering Thermal Engineering Chemical Reaction Engineering Process Systems Engineering and Safety Biochemical Food and Medical Engineering Micro and Nano Systems Materials Engineering and Interfacial Phenomena Energy Environment Engineering Education
Controlling protein adsorption on polymeric surfaces is one of the major challenges for developing novel biomaterials. To clarify microscopic mechanisms on the suppression of protein adsorption, identification of crucial factors for achieving excellent biocompatibility is significant. In this study, free energy calculations are introduced to assess the biocompatibility of two types of material: poly(2-hydroxyethyl methacrylate) (PHEMA) and poly(vinyl alcohol) (PVA). Free energy profiles are evaluated for an amino acid residue approaching their repeat unit in explicit water molecules from molecular dynamics simulations. Although PHEMA and PVA are generally both hydrophilic, the predicted profiles become remarkably different from each other. The profile for PHEMA shows nearly flat profiles, while the profile for PVA shows energetically stable minimum. These results theoretically demonstrate that hydrophilicity is not a crucial factor for achieving excellent biocompatibility, although it can be a prerequisite.
The matrix enhancement effect was observed and is dependent upon certain conditions in the measurement of vapor–liquid equilibrium (VLE) by headspace gas chromatography (HSGC). A method compensating the matrix enhancement effect around the pure component region, where the matrix enhancement effect often appeared, was proposed without loss of convenience of HSGC. The VLE data obtained by this method were in good agreement with the literature values and thermodynamically consistent.
We examined the shear stress in toluene-based suspensions of titanium dioxide nanoparticles/poly(vinylacetate) subjected to constant shear rates. The stress exhibited periodic oscillations at shear rates in a certain range. The amplitude of these oscillations first increased and then decreased with increasing ethanol concentration in the suspensions, indicating a transient stress response that was tunable by utilizing the solvent composition. In the oscillation mode, the suspensions showed a viscoelastic, shear-thickening behavior. The composition-dependent stress oscillation is discussed on the basis of hydrodynamic and depletion attractions among the particles.
In this paper, a new method for exergy analysis of distillation column is presented. It is based on the principle that there are exergy losses wherever driving forces exist. An approach has been developed for investigation the effects of various parameters on exergy losses. For verification of results, some experiments were performed using a pilot plant. The thermodynamic simulation of this pilot plant has been performed using a simulator. A thermodynamic model has been used to calculate the exergy losses of each stage in this simulation. The experimental and model results were observed to be accurate and in good agreement. The results showed that the use of the model results in more accurate analysis than the prevalent streamwise method exergy analysis in distillation column does. Both calculations and experiments showed that heat transfer in reboiler and condenser, and mass transfer are the two most significant sources of exergy losses.
A new isotherm equation was developed in the framework of the vacancy solution theory. In the model, vacancies in the adsorbed phase are dealt with as a kind of reactive entity, which leads to more flexible adsorption isotherm equations. The model can be called Reactive Vacancy Solution Theory (RVST). Practical RVST equations for single-component and binary-component adsorption systems were derived by using Wilson or NRTL equations for activity coefficients. The RVST equations were found to coincide with the Langmuir–Freundlich equation when the effect of activity coefficients is assumed to be negligible. Furthermore, it was also found that the RVST model conforms to other isotherm equations when it is simplified. Validity of the RVST-W and RVST-N equations for single-component adsorption was tested. The results suggest that the highly non-ideal adsorption data are successfully correlated using the RVST models.
Biosorption, which takes place by passive metal-sequestering, has recently attracted attention as an alternative to conventional separation processes such as precipitation and solvent extraction. In this work, the adsorption behavior of rare earth elements (REEs) onto an Escherichia coli (E. coli) bacterial adsorbent was examined. The adsorption of REEs increased with increasing pH of the feed solution. Also, E. coli showed a higher selectivity for heavier REEs, especially for scandium. To elucidate the adsorption mechanism, the functional groups on the E. coli were chemically modified. By analyzing the adsorption behavior and FT-IR spectra of these E. coli samples before and after adsorption, it was confirmed that REEs are adsorbed at phosphate and carboxyl groups on the surface of E. coli. It is concluded that E. coli is a potential novel adsorbent for REEs.
The optimization of the oxidative esterification of propionaldehyde to methyl propionate using a supported palladium catalyst in methanol under heavy-metal-free and pressurized-oxygen conditions, which we recently reported in a previous paper, were carried out together with a study of the reaction route, the nature of the catalytic active sites, and the effect of the support. In our previous paper, we reported significantly improved activity for oxidative esterification using commercially available 5% Pd/Al2O3 at 1.5 MPa of O2 gas and 333 K and emphasized that the doping of 5% Pd/Al2O3 with lead was not needed for the reaction system, but we could not improve the activity that was reported when using 5% Pd/γ-Al2O3 doped with 5% Pb (a 93.2% conversion of propionaldehyde, 76.8% selectivity for methyl propionate and a 71.6% yield of methyl propionate) at 0.3 MPa of O2 gas and 353 K, as reported by another laboratory. In the present study, however, we exceeded those values and obtained a 98.3% conversion of propionaldehyde, 75.3% selectivity for methyl propionate and a 74.0% yield of methyl propionate using 5% Pd/γ-Al2O3 undoped with Pb at 1.5 MPa of O2 gas and 333 K. It should be noted that, in the preparation of the present 5% Pd/γ-Al2O3, Pd was doped onto Al2O3 that had been previously treated with aqueous NaOH. Another active alumina support, η-Al2O3, prepared from boehmite, afforded activity that was substantially lower than that of γ-Al2O3 and depended on the calcination temperature of boehmite to η-Al2O3. When using various concentrations of CH3OH in the aqueous reaction solution, the oxidative esterification proceeded through the formation of propionic acid. To determine why the Al2O3 support afforded better activity than the active carbon support, Pd/Al2O3 and Pd/C catalysts were examined by XAFS (X-ray absorption fine structure). XAFS revealed that Pd on Al2O3 shows a better redox nature than Pd on C, which resulted in activity on Pd/Al2O3 that was better than that on Pd/C.
The transesterification of triglyceride with methanol using an alkali catalyst was experimentally measured, and the obtained equilibrium constants were analyzed by the van’t Hoff model. The constant for the conversion of triglyceride to diglyceride was the smallest. The standard enthalpies of formation in the transesterification were measured to be positive, i.e., the reaction is endothermic. Next, the transesterification using the countercurrent multistage reactor system was computationally simulated with the equilibrium stage model, in which the equilibrium constants obtained above were used. The concentrations of the triglyceride remaining in the biodiesel fuel product drastically decreased by the reactor staging, and consequently the reaction temperature and the required amount of methanol could be reduced. The transesterification by the countercurrent multistage reactor was found to be attractive because of the efficient production of the biodiesel fuel.
Bayesian statistical inference is applied to the process model parameter estimation. It is expected that the Bayesian inference framework provides a way to properly handle the nonlinearity of the process models. Posterior probability distribution of the estimated parameters is computed by quasi-Monte Carlo (QMC) simulation of the likelihood function. Maximum a posteriori (MAP) or marginal posterior mode (MPM) estimate of the model parameters is identified that is believed to be more robust than the maximum likelihood estimate (MLE), especially under circumstances where the number of experiments or data points are limited. The confidence region that is evaluated by the proposed method would not necessarily be ellipsoidal as is, however, the case with the conventional or “conservative” Cramer-Rao lower bounds (CRLB) that are calculated by approximation or linearization of the Fisher information matrixes (FIM). It is shown that the “correlation” among some of the estimated parameters can be explained by the “singularity” that is inherent to the model assumed for the investigation. The MPM estimate, in particular, of each parameter can be computed even under such (partially) singular model situations.
Controlling excess air ratio is a challenging issue in maximizing energy efficiency of a reheating furnace. This paper proposes a new methodology for controlling the excess air ratio using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor while most industries have been using Zr sensor. An oxygen concentration feedback control system using the methodology is also developed and implemented in real reheating furnaces in South Korea. The proposed TDLAS sensor control system turned out to be better than a Zr based one in terms of maintaining the desired performance steadily throughout the entire heating region of the reheating furnace.
For strategic chemicals management in process design, the chemical risk and environmental impact due to the use of chemicals must be identified and controlled. Although various scientific methodologies evaluating such issues have been developed, a single application of one of the methodologies cannot address all of the adverse effects. In this paper, we classify chemical risks using suitable scientific assessment methodologies for risk trade-off analysis on the basis of the features of adverse effects. On the basis of this classification, current problems can be identified through the understanding of differences in the existing chemical risk and environmental impact. An actual case study of agent selection in metal-cleaning process design has been performed. It is thereby demonstrated that risk trade-off among agents can be visualized to distinguish agents. In appropriate agent selection, all adverse effects associated with the use of chemicals can be considered with such a visualized risk trade-off. Risk trade-off analysis based on the classification of risk enables the identification of increased and decreased risk upon substituting chemicals and processes. This analysis can be used as a foundation for the discussion of practical strategies on chemicals management in process design.
Since the optimal construction of a Radial Basis Function Neural Network (RBF-NN) is difficult to determine and plays an important role in predicting performance, we propose a modified RBF-NN, which is integrated with the K-Means clustering based on the Rough sets theory (Rough K-Means), in order to optimize the number of hidden neurons. First, an original RBF-NN that superposes each center to a training set point is built and the network is trained to obtain the potential relationships between the input and output variables. Next, Rough K-Means is employed to optimize the structure and weights of the RBF-NN by clustering the output from the hidden layer that is due to the cluster uncertainty of the hidden output. Further, RBF-NN with Rough K-Means and K-Means, respectively, are employed to develop naphtha dry point soft sensors. The results show that the Rough K-Means is more effective in handling uncertainty and that RBF-NN with Rough K-Means is superior to RBF-NN with K-Means.
Dimethylaminoethyl methacrylate (DMAEMA) was graft-polymerized at a density of 1.7 mmol/g onto a 6-nylon fiber by radiation-induced graft polymerization. Urease was bound to the resultant anion-exchange fiber by electrostatic interaction. After crosslinking with transglutaminase, 80% of the bound urease was immobilized at a density of 41 mg/g of the fiber. Urea in water was quantitatively hydrolyzed during the permeation of a 0.20 mg/L urea solution through the urease-immobilized-fiber-packed bed with a diameter of 5.5 mm and a height of 27 mm in the residence time range of 12–180 s. The activity of immobilized urease did not deteriorate after repeated use of the fiber, i.e., during reaction and storage.