Introduction to multivariate data analysis in chemical engineering
Barriers to use of multivariate methods
Multivariate methods are used today in the chemical, pharmaceutical, oil and gas, petroleum refining, mining and metals, pulp and paper, agriculture and food industries, to name a few.
However, due to their sophisticated nature, multivariate analysis has predominantly been used by scientists in R&D or Technical departments. This is because applying these techniques requires knowledge of the most appropriate methods for different data types, developing models, interpreting plots etc. Historically, these skills have not been a focus for chemical engineers, who have tended to use first principle models.
Collecting data systematically and being able to get it into a format suitable for analysis was another common obstacle to using multivariate methods in the past. This is less of an issue today because most processes are instrumented and sophisticated control systems are widely used. In fact, the challenge now is that so much data is collected that it is increasingly difficult to cut through the huge amount of information to find the underlying patterns, which is driving the use of multivariate methods.
Why is the situation changing?
While control systems and analytical instruments have improved greatly in recent years, the software and techniques used to analyze increasingly large, complex data sets has not evolved at a similar pace to the improvements in hardware and control systems.
Today, however, leading companies are looking for new sources of competitive advantage and realizing that the enormous amount of data collected during their production operations offer great insights to improve product development and process performance.
Additionally, companies are under increasing pressure to improve the sustainability of their products and processes, which can be achieved with greater insight offered by more powerful analytical tools.
Similarly, thinner margins mean companies are constantly looking to drive out costs by running processes closer to limits, using lower cost components where possible, reducing energy use and trying to minimize waste and rework costs.
Recent technology changes have enabled multivariate models to be developed by specialist groups and then applied to real-time process data. These tools can be integrated directly with instrumentation or as part of a larger control system, with results displayed in a choice of formats ranging from operator to expert view.
Advantages of multivariate data analysis
Multivariate data analysis provides a simpler yet more accurate view of the overall process health. It allows users to identify variables that contribute most to the overall variability in the data, essential for understanding complex data or processes. It helps isolate those variables that are related i.e. co-vary with each other, which can then be taken into account in further model development and analysis.
Multivariate data analysis is highly visual in nature. Rather than simply displaying statistics, it shows data plotted in a variety of forms, so patterns are easier to see and aiding in interpretation.
Traditional univariate control charts show many different variables simultaneously, making it extremely difficult to get a clear, complete picture. Multivariate control charts condense all of this data into one or two plots, taking into account the complex interactions between variables. If the process begins to drift, it is possible to ‘drill down’ into the specific samples or outliers to quickly identify the root cause of the problem using a combination of multivariate and univariate diagnostics.
Benefits of applying multivariate models to chemical process monitoring
Multivariate data analysis can be used across the value chain of chemical engineering from product development, scale up or scale down, process engineering and process optimization. Manufacturers who have adopted these tools can quickly see improvements in their operations and on their bottom line.
Increased yields: Identify which combination of process variables produces the highest yield, and improve process understanding generally to find the optimum settings.
Improved quality: Root causes of quality problems can often be difficult to identify. Multivariate analysis gives deeper insights and can pinpoint the variables or their interactions causing problems. When combined with spectroscopy, it can enable cost-effective 100% quality testing.
Improved product safety and sustainability: Reduce the use of hazardous chemicals, minimise scrap and optimise processes for more sustainable products.
Reduced process failures: Identify issues in a process before they become problems causing the process to fail, and use powerful multivariate diagnostics to drill-down into the cause of the problem.
Reduced variability: Maintain consistent end-product quality more effectively by early correction of drifts in process using multivariate predictive models.
Cheaper product development: Experimental design (DoE) combined with multivariate analysis lets you develop products faster and more cost-effectively, by reducing the amount of tests and experiments needed.
Faster time to market: Move products from the pilot plant to full production scale faster. Multivariate analysis provides the product and process insights needed to help make scale up smoother to get to market faster.
How to get started with multivariate data analysis
Virtually all manufacturers collect a large amount of data, but in many cases it remains unused in ‘silos’ or analyzed with insufficient tools. Implementing multivariate methods into a process environment starts with answering fundamental questions such as:
- What data are being collected
- How is the data available i.e. is it in paper reports, on local disks or accessible in a control system
- What is the organization/department trying to achieve, i.e. what are the pressing issues
With basis in these questions a problem statement is easily defined, and plans for a better use of the data addressing relevant issues can be created. The speed and affordability of developing and implementing multivariate monitoring and control strategies is comparable to those for univariate programs, but a multivariate approach gives manufacturers significantly better understanding of their processes, as well as more robust and sensitive control strategies.