Unlocking Production Efficiency: How Data Analytics Enables Process Control Improvement

The application of data analytics to the field of process control has emerged as a transformative force. Whether due to the overwhelming amounts of available process data or to the thinning out of operational staff, software tools that continuously monitor performance at the level of the PID control loop have demonstrated significant value to manufacturers across the process industries. The ability to identify subtle changes in control through the use of analytics has enabled manufacturers both to mine their abundant stores of data and to equip production staff with actionable intelligence.

PlantESP is among the industrial automation market’s leading control loop performance monitoring (CLPM) technologies. PID controllers provide the foundation for effectively and efficiently regulating complex production processes. CLMP solutions apply data analytics that empower manufacturers to optimize their PID-driven processes and maintain enhanced plant-wide performance while advancing their internal productivity standards. Consider the following:

Real-Time Decision-Making

A key benefit of applying data analytics to process control is the ability to make sense of the enormous amounts of available data and facilitate real-time decision-making. Traditionally, operators relied on predefined rules and periodic manual inspections to adjust processes. The human eye is often incapable of distinguishing subtle changes in control that routinely occur. However, with the use of advanced analytics, tools like CLPM tease out changes that negatively affect performance. That allows for swift identification of anomalies or inefficiencies, and it facilitates prompt adjustments to maintain optimal operating conditions.
For instance, in pharmaceutical manufacturing the application of CLPM can identify issues in the performance of mechanical assets such as valves before malfunctions occur. Advance warning of mechanical issues helps with the prevention of costly downtime and ensures seamless production. Similarly, in the mining industry, real-time monitoring of controller performance optimizes the operation of heavy machinery, reducing energy consumption and extending equipment life.

Moving Beyond Maintenance to Continuous Improvement

Data analytics is increasingly a part of enterprise-wide improvement initiatives. Multi-site deployments of analytical solutions continue to rise as they aid process manufacturers in their efforts to continuously assess and improve. By analysing patterns and trends in performance, analytical tools help process engineers to maintain hard won performance gains. Select analytical tools adapt to upgraded performance benchmarks and automatically seek new opportunities for improvement. Relative to process control, this approach to continuous refinement allows for steady reductions in waste and energy consumption without compromising either output or quality.
CLPM technologies consistently help process manufactures to maintain their gains and to continuously seek higher levels of performance. As an example, through the application of analytics, manufacturers from the pharmaceutical sector have uncovered challenges with mixing processes, leading to adjustments that further reduced batch cycle times and improved overall yield. Similarly, in the mining sector, the use of CLPM tools has allowed production staff to steadily tighten control around controller Set Points and shift them closer to constraints. These adjustments have enabled improvements in ore recovery and operational costs.

Leveraging Advanced Technologies

In industries like pharmaceuticals, mining, among others, the application of data analytics to process control represents a paradigm shift. By harnessing real-time insights and fostering continuous improvement, organizations can unlock new levels of efficiency and competitiveness. Embracing these innovations not only optimizes operations but also ensures regulatory compliance and sets the stage for future advancements in industrial automation and sustainability.

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