The testing standards, ISO/TS 21346 of individualized cellulose nanofibril (iCNF) materials were published in 2021 while their commercialization is progressing. Japanese National Standardization Body had proposed a project of developing the standards to ISO in 2017, and a domestic drafting team has developed the standards based on its own development methodology. In this paper the methodology shared as implicit knowledge by the drafting team members is systematically described as explicit knowledge. It is specifically expressed as a series of logical processes comprising identification of elemental issues to be considered, step-by-step integration of the elemental issues and the eventual target of publishing coherent and harmonized testing standards for iCNF materials. Additionally, the contents of ISO/TS 21346 resulting from the methodology are concisely reviewed with perspectives of further development in the future.

This paper considers the conversion problem from strictly proper non-structured Linear Time-Invariant (LTI) controllers, which are designed a priori, to strictly proper “observer-structured LTI controllers”, whose structure is similar to but not exactly the same as the so-called “Luenberger observer-based LTI controllers”, for LTI Parameter-Dependent (LTIPD) plant systems. We parametrize the state-space matrices of the observer-structured controllers with those of the non-structured controllers, one free matrix and a state transformation matrix, and propose a method to obtain the optimal state transformation matrix with respect to the convergence of non-zero initial plant state. We also clarify the relation between our method and an existing one for full-order controllers for LTI plant systems.

This paper is introducing a new practical method for robust I-PD tuning based on the process dynamics model with the model parameter uncertainty. Several types of process dynamics models in the process control field are considered. These are 1st-order lag, 2nd-order lag, integrating system with 1st-order lag, 2nd-order unstable system, and all of which include time delay. The PID settings formula of the I-PD controller is derived to track the reference trajectory by the direct partial model matching method. Given the nominal process model with parameters uncertainty range, the robust PID settings are determined, which provides stable and good control performance over the entire process. For this purpose, the worst process whose control is most likely to become unstable, and the 2nd-worst process whose performance is the worst with the PID settings that stably controls the worst process are searched. If the PID settings based on the 2nd-worst process ensure stability in the worst process, the control performances over the entire process become approximately equivalent. In addition, the maximum overshoot value of the manipulated variable controlling the nominal process is utilized to design the time constant of the reference model and to adjust the control performance properly. A design example is briefly presented to illustrate the applicability of this method.

The objective of this paper is to predict the turbidity after flocculation from floc images in jar-test deep convolutional neural network (DCNN). Our goal is to develop a system to control the water purification process using the predictive model. In conventional studies, chemical parameters such as pH and alkalinity are generally used to predict turbidity. However, our proposed method does not use chemical parameters. It uses images of floating matter, called “floc”, generated during the flocculation process as input to the DCNN. We performed experiments using DCNN to predict turbidity after flocculation from images of “flocs” generated in jar-tests. We used VGG-16 as the DCNN for our experiments. Furthermore, we conducted experiments to compare the proposed method with the baseline method using chemical parameters.

As a result, 1) we revealed that the turbidity after flocculation can be predicted by using the image during flocculation as input to VGG-16; 2) we revealed that the optimal period to be used for the prediction model was the data 200 second after the start of the jar-tests; 3) we revealed that our proposed method can predict turbidity with better accuracy than the baseline method using chemical parameters.