Application Example: Optimization of Process Control Using Soft Sensor Technology
A soft sensor technology applying automated machine learning to build a prediction model for selection rates. This enables the optimization of process control, achieving cost reduction and increased productivity.
Application examples of soft sensor technology in the chemical manufacturing industry. In the manufacturing process of chemical products, the catalyst regeneration operation for hydrogenation reactions was carried out according to a schedule, but there were challenges related to increased workload and the costs of switching catalyst towers. Therefore, by using soft sensor technology, a predictive model for future selectivity values was constructed, enabling the estimation of catalyst performance indicators, and optimizing the regeneration frequency, which resulted in improved productivity.
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Related: Soft Sensor Technology (Estimation Model Construction/Calculation Tools) Estimation model construction/calculation tools for soft sensors. For the first time in the industry, automated machine learning has been applied to the model generation process, enabling automatic continuous estimation of target values that are difficult to measure in a short time and at regular intervals, such as sampling in manufacturing sites, and achieving visualization of data. It supports the optimization of "operations," "equipment management," and "energy utilization" in the fields of chemicals, steel, food, and pharmaceuticals.
Applications/Examples of results
[Challenges Before Implementation] Hydrogenation reaction catalysts gradually lose performance, necessitating periodic catalyst regeneration operations. Currently, catalyst regeneration is performed based on a predetermined schedule, but there are challenges related to reducing the workload and the costs associated with switching catalyst towers. It was necessary to optimize the regeneration frequency by using catalyst performance indicators. [Effects of Implementation] By constructing a predictive model for the future selectivity rate, it became possible to optimize the regeneration frequency based on catalyst performance indicators. This led to cost reductions through extended catalyst life and the elimination of unnecessary switching time, thereby improving productivity. A model that captures the trends of actual measured values has been developed, and efforts will be made to promote its application in actual operations.
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Case Study: Optimization of Process Control Using Soft Sensor Technology
Current Status and Challenges of DX in Manufacturing Management in the Chemical Industry
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Providing solutions for various plants and factories such as food, chemicals, oil and gas pipelines, paper and pulp, and cleaning facilities. Our measurement and control systems have over 40 years of history since the launch of the first distributed control system in 1975. Based on our track record, we propose systems and solutions to support the latest technologies, IoT utilization, and the development of smart factories. - Steam generation heat pumps / waste heat utilization / visualization of thermal energy - Clamp-on steam flow meters / flow meters / flow sensors / thermal balance analysis - Industrial furnaces / electric furnaces / high-frequency induction furnaces / IGBT power supplies - Gas analyzers / gas measurement devices / gas analysis sensors - Predictive maintenance / anomaly detection maintenance / preventive maintenance / maintenance and prevention systems / smart security - Smart factory - MES / DCS / SCADA / edge controllers - EMS / energy management systems / visualization of energy - Smart glasses / remote work support systems - Multivariate analysis / MSPC - Engineering tools