Utility usage in high-tech manufacturing facilities is key area where most energy are being consumed. Energy efficiency by optimizing the generation and usage of various utilities holds the key to lower energy consumption. With software modelling that take into accounts operational, economic and environmental penalty constraints, coupled with real-time actual production demand and forecasts, an optimal utility plan can be calculated every half-hourly during a day. Each optimal utility plan generated is fed as Targets to an advanced process control system that uses Embedded MILP Optimizer and AI Deep Learning to drive multivariate process variables of the plant towards the Targets in a way that minimize fuel consumption and stabilize demand and supply. This cycle repeats itself every half-hourly to automatically and continuously optimize plant utility.
Equipment failures and process disruptions are the main drivers of unplanned downtime that costs the industry billions of dollars in lost revenue every year. Whenever equipment failure occurs in the plant, those transitory periods produces excessive levels of greenhouse gas emissions, particularly from flaring, or venting of excess steam to prevent over-pressure. In addition, Equipment having deteriorated health condition consumes more energy to work. As a result, companies rely heavily on preventive maintenance to avoid equipment failure. However, not only preventive maintenance is very costly, simply opening a machine for inspection can cause problems that did not previously exist. With advancement of AI and Machine Learning, autonomous AI/ML agents can be deployed online to recognize minuscule pattern changes across multiple sensors to detect early symptoms of equipment degradation that neither humans nor sensors alone are capable of detecting. These AI/ML agents can predict when and how equipment will fail and initiate early intervention to avoid the damage. Early attention may only require a simple maintenance task, and in some cases, notification will occur weeks in advance, allowing machines to be taken offline at a convenient time when production demands are lower, the result is lower repair costs and increased overall production.