There has been continual growth in the use of digital technologies in process manufacturing. This has ranged from increasingly reliable and low-cost sensors, digital field networks, control and shutdown systems, process historians, model-based optimizing controllers, and manufacturing information systems, to enterprise resource planning systems in the last few decades. These technologies have enabled higher process performance, reduced operating risk, and improved staff productivity.
Improvements in these technologies continue:
Process Automation Processes
- Continued improvements in self-diagnostics, error prevention, precision in field instruments, and valves and continued cost reduction and instrument asset management.
- Better control systems capabilities such as configurable I/O and tools that ensure control systems are no longer on the critical path of new build projects. Better tools and new modules to reduce the cost of system maintenance, migration, and sustaining IP in existing systems. Better approaches and technologies to build operator interfaces.
- Better control loop maintenance and advanced control, including distributed MPC and site-wide real-time dynamic optimization.
- More integrated manufacturing execution systems.
- Better technologies and approaches to manage cybersecurity risk.
Some trends will impact the process industries in the broader world and enable further performance improvements and help mitigate the loss of expertise. They will also contribute to the improved sustainability of these industries.
Technology Development: Big Data and Analytics
The trends in digital computing and mobile technologies now mean huge amounts of information about people and their behaviors, appliances and equipment, and the environment. Increasingly, companies are analyzing these data to help make better decisions faster. We are all familiar with internet-based systems recommending other purchases based on the items we have selected, or adverts presented to us based on entries made.
Similar opportunities exist in manufacturing with some specific challenges:
- To be successful, subject matter experts need to use modern analytical tools. Subject matter experts such as engineers, managers, and operators seldom have a strong background in statistics, machine learning, and AI. Similarly, data scientists seldom have a good understanding of process manufacturing. Tools are needed that enable subject matter experts to perform analyses and yet collaborate with data scientists on the more complex problems.
- Data is stored in disparate systems. There are usually data quality issues, including missing data, different sampling rates, and temporal misalignment, signal and process noise, measurement uncertainty, and the relationship with business risk. Tools need to make it easy to access, align, and cleanse data.
- Process analysis needs to make it easy to answer common questions but in ways that make sense by, for example, allowing for grades, operating modes, and feedstocks.
- Analysis needs to allow fast results to be achieved, effective collaboration, issues to be communicated quickly, and improvements to be driven rapidly.
Increasingly, tools are becoming available that meet these needs and enable engineers and managers to analyze performance without the grind of trying to use spreadsheets or become data science experts.