Last year, we introduced energy conservation by optimization technology and demand response solution to support the clean energy transition towards carbon neutrality. This year, as the second report, we will focus on utility plant operation optimization by Azbil’s optimization solution, which has a large effect for energy conservation and CO
2 emission reduction.
Firstly, we realize utility plant operation optimization by using Multivariable Model Predictive Control (MPC). A hybrid system consisting automatic control and guidance built by using MPC provides flexibility that enhances receptivity among operators on-site. Additionally, our optimization solutions also utilize soft sensors, digital twins and AI technology to solve problems often occurred in utility plant operation optimization.
In this paper, we classify the problems that have been solved through our optimization solutions in many industries such as Pulp and Paper, Refinery, Chemical, into several topics which are “Boiler master control”, “Building and updating model for MPC”, and “Receptivity among operators on-site”, and report the solutions for challenges indicated in these topics.
In particular, regarding “Building and updating model for MPC”, which is directly linked to the effectiveness of implementation of MPC on-site, we will introduce in detail for the utilization of digital twins in building model expressing the equipment property which represent the actual behavior of the utility plant, and the newly developed automatic model updating function through AI technology to update the model for MPC according to changes in equipment property due to reasons such as aging and maintenance.
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