Taketoshi Kurooka

COMPARISON OF THE SEPARATION PERFORMANCES OF A MULTI-EFFECT BATCH DISTILLATION SYSTEM AND A CONTINUOUS DISTILLATION SYSTEM

Abstract A new type of batch distillation system which separates multiple components simultaneously is proposed. The features of this system are that the steam is supplied to the reboiler of the first column only, and a condenser of one column is heat-integrated with the reboiler of the next column. Therefore, the energy required to separate multiple components is markedly less than that of an ordinary batch distillation system. The energy consumption of the proposed system is compared with that of an ordinary continuous distillation system. Through simulation, it is found that the proposed system has the potential to have a better separation performance than a continuous system if the number of products is increased.

Dynamic simulation and nonlinear control system design of a heterogeneous azeotropic distillation column

Abstract A nonlinear control system was considered for a heterogeneous azeotropic distillation column that a three component mixture of water, n-butyl-acetate, and acetic acid. This system shows such complex dynamic behavior that it is difficult to operate it and to design its controller. In this study, we developed a dynamic simulator to understand and characteristics of the process. After considering the systems behavior by using the simulator, a control system was developed with exact input—output linearization. The control performance was examined when a step change happened in the feed composition and the feed flow rate. Compared with a multiloop control system, the proposed control system can quickly settle down at the setpoints with small fluctuations.

Mind state estimation for operator support

Abstract We have developed a method to estimate a plant operators thinking state during abnormal situations. The operators thinking state is estimated from EEG data. First, three basic modes were defined according to the typical thinking state of a plant operator at the time of a malfunction. Second, experiments were performed to evoke the basic modes. Third, a nonlinear model was developed to estimate the thinking state of a plant operator. Finally, the estimation method was applied to a subject who operated a boiler plant simulator under abnormal circumstances. It was verified that the method gives useful information for estimating the plant operators thinking state.

Application of Mind State Estimation to Plant Operators

Abstract We developed a linear regression model with Electroencephalograms(EEGs) to estimate the mind state of a plant operator. First, we defined three modes according to the typical thinking state of plant operators. The classification was validated by preparatory experiments with card games, mathematical problems, and puzzles. The estimation method was applied to a plant operator who operates a boiler plant simulator under abnormal situations. As a result, the method provided the plant operators mind state, which is liable to be overlooked by analyzing with a simple interview and observation.

Mind state estimation in logical thinking jobs

When a human logically solves a mathematics problem or a general problem, the state of thinking in the brain will change according to the level of difficulty and familiarity of the problem. In this research, we have tried to define the mind state in logical thinking jobs and measure the level of the state with electroencephalograms (EEGs). First, three basic modes were defined based on typical states of logical thinking. Second, this classification was validated by cluster analysis. Third, a linear regression model was developed to indicate the thinking state of a plant operator. Lastly, it was verified by experiments that the model gives useful information for estimating a humans thinking state.

Modeling and simulation of human errors in plant operations

Abstract We have developed a cognitive information processing model that incorporates the states of mind and body in order to simulate a plant operators behavior under abnormal situations. This model enables simulation of human errors under various conditions. As an illustrative example, oversights in using two graphic panels designed for monitoring a boiler plant were examined. The results coincided qualitatively with observations of actual plant operations. This operator model can be used to analyze various types of human errors from the viewpoint of cognitive information processing and to study how to cope with different situations.

Thinking state monitoring using plural physiological signals

In this paper, we present a method for monitoring the human thinking state, which uses plural physiological signals. First, an experiment was performed over three days to obtain ECG (electrocardiogram), EOG (electrooculogram), and RSP (respiratory activity) data when a subject was solving a mathematics problem. Second, it was confirmed that the three physiological signals had characteristics corresponding to the basic modes of the thinking state. Third, various types of thinking state estimation models were built using one or three physiological signals, and the models were evaluated. As a result, it was found that the model which used ECG, EOG, and RSP together to monitor the human thinking state was the most reliable.

Support systems for knowledge inheritance from video images of plant operations

Abstract It is difficult to extract tacit knowledge in the workplace. In this paper, we apply video annotation to externalize the technologies and skills in plant operations. We developed two prototype systems. The first is an interactive support system using multimedia, which offers functions for collecting know-how and know-why in on-site plant operations, and for presenting this information as learning materials. The second is a system for evaluating emergency training results and extracting the common problems and lesson points. This system is useful for sequential data analysis related to human behavior.

Condition monitoring of a heterogeneous azeotropic distillation column using a self-organizing map

Abstract A method is proposed to monitor the conditions of a chemical plant by considering the relationships among many types of process variables. The proposed method was evaluated in case studies involving a simulator on a heterogeneous azeotropic distillation column. First, a set of good-condition data and ill-condition data of process variables was stored. In this process, a classifier algorithm was applied to select a set of principal process variables representing the process condition. With the selected variables, the input vector for a self-organizing map algorithm was defined. Second, a set of typical patterns of good-condition data was generated by using selforganizing map with the stored good-condition data. Third, the condition of the column was monitored by calculating the dissimilarity value between the test data and the typical patterns of good-condition data. Consequently, it was clarified that the proposed method provides effective information on plant conditions in the early stages.