The correlation equation of Aida and Shono (2018) is expanded to propose a novel correlation equation related to the minimum impeller speed for a liquid–liquid agitated vessels. The correction coefficient of baffle k_b was added to the original equation in order to expand the applicability. This k_b is a function of B/D, 2c_b/(D–d), h/H, k_s, and n_b (c_b: clearance between baffle and vessel wall, B: baffle width, D: vessel diameter, d: impeller diameter, H: liquid depth, h: height of baffle under the liquid surface, k_s: correction coefficient of baffle shape, n_b: number of baffles).

Using this correlation equation, the previous literature values could be accurately sorted. In addition, the estimation of the minimum impeller speed with incomplete baffle conditions, which had been conventionally difficult, has become possible.

Plate heat exchangers are widely used in general industries such as heat recovery applications as energy-saving equipment, which can contribute to a low-carbon society. Many plate heat transfer surfaces suitable for various applications have been developed thus far, and while high performance can be obtained by vortex flow due to turbulence, the pressure loss also increases as a trade-off, and thus it has become an issue to suppress pressure loss as much as possible commercially and transfer heat efficiently.

This time, the authors have devised and prototyped a plate waveform pattern in which the fluid becomes a longitudinal vortex, then measured the flow velocity in the plate by LDV measurement using a transparent acrylic board made by tracing the actual plate flow path, and visualized the flow uniformity. We also produced stainless steel plates and verified the difference in heat transfer and flow performance of herringbone.

Poorly tuned PID parameters often cause self-excited oscillation. We have already reported a method to obtain a closed-loop identification model from self-excited oscillatory time-series data. We have also reported a method to de-sign PID parameters based on IMC theory. However, automatic detection of the self-excited oscillatory control loops had been an unsolved problem. In this work, we propose an automatic method to detect the damped sine wave response from time-series data. The proposed detection method was implemented with the closed-loop identification method and the PID parameter design method. The method was first applied to the simulated vinyl acetate monomer (VAM) process and all the self-excited oscillations were well detected automatically. Then, the method was applied to thousands of real industrial control loops, whereby tens of poorly tuned controllers were found within several hours, suggesting re-tuned PID parameters. An example of the control response of the re-tuned PID controller with the suggested parameters shows great improvement of the control performance.

In the operation of a chemical plant, the quality of the products must be maintained at a constant level and the production of off-specification products should be minimized. For this purpose, it is necessary to measure process variables related to product quality, such as temperature and composition of materials at various parts of the plant, and perform appropriate operations (i.e. control) based on the measurements. Some process variables, such as temperature and flow rate, can be measured continuously and instantaneously; however, other variables, such as composition and viscosity, can only be obtained through time-consuming analysis after sampling substances from the plant. Soft sensors are proposed to estimate process variables that cannot be obtained in real-time from easy-to-measure variables. However, in the statistically constructed soft sensors from recorded measurements, the estimation accuracy in unrecorded situations (extrapolation) is not guaranteed and deteriorated in general. In this paper, to improve the extrapolation performance, we propose to use internal state variables of a plant as soft sensors by estimating them using a dynamic simulator that can estimate and predict even unrecorded situations based on chemical engineering knowledge, and reinforcement learning, one of the AI methods. Additionally, we describe the prospects for plant operation and control using such soft sensors and the methodology to obtain the prediction models (i.e. simulators) required for the proposed system.

Disodium hydrogen phosphate dodecahydrate is as a latent heat storage material that is expected to be applied to indoor air conditioning and floor heating, especially medical applications. However, the heptahydrate, which has a different number of crystal water from the dodecahydrate, can exist stably at a temperature higher than the melting point of the dodecahydrate. Owing to the crystallization and the subsequent accumulation of the heptahydrate in the dodecahydrate melt, during a long-term thermal cycle of heat storage and heat release, the amount of latent heat of the dodecahydrate gradually decreases. In this study, in order to suppress the occurrence of such phase segregation, the melting and freezing behavior when applying a thermal cycle to the dodecahydrate was observed in detail. As a result, it was clarified that the occurrence of phase segregation not only reduces the latent heat amount of the dodecahydrate, but also lowers the nuclearization temperature thereof. The occurrence of phase segregation due to the crystallization of heptahydrate was thought to be due to the decrease in the diffusion rate during the dissolution process of the heptahydrate. It was suggested that, if the concentration polarization of the Na₂HPO₄ component forming in the melt was eliminated, for example, by installing a stirring mechanism in the heat storage device, the liquidus line of the heptahydrate would be substantially dissolved and the occurrence of phase segregation could be prevented.