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Process Optimizations That Help Chemical Manufacturers Weather the Corona Crisis

April 30, 2020

In the midst of a global crisis, many industrial manufacturing operations— including those in the chemical industry— are faced with shortages of supplies and equipment, or staff reductions, and finding it difficult to keep operations working as normal. Are there process improvements or tools that can be used to manage production more efficiently during this time of COVID-19 (and moving forward)?

utilize data through chemical plant to optimize processes

How can you find the most cost-effective ways to keep your plant running efficiently during times of lean staff or limited supplies?

Even if your plant is up and running, chances are it’s operating under conditions that are less than ideal, or you’re operating very lean. You may be running far below full capacity, have less staff than normal, or may have difficulty getting delivery on raw materials, feedstock or replacements for parts or equipment that breaks or stops functioning. Yet you have fixed costs – and operational demands to meet.

What you need is to find more cost-effective ways to keep your equipment running optimally and to be able predict when down times or end of equipment life may occur. You need reliable data-backed guidance to adjust your production processes in order to make more efficient use of raw materials, increase your output, and reduce wear and tear on your equipment.

This can be especially important if you’re working in an industry with batch processes. In areas such as specialty chemicals, fine chemicals, coatings, silica, resins and polymers, processes may rely on manual steps or be affected by gaps in production. Are variations in how these steps are managed causing you to loose efficiency? How can you tell? You need a solution that relies on advanced data analytics to gain more effective batch control.

[Related: Should your site be using Six Sigma to improve production quality and reduce costs?]

Data Analysis Can Improve Processes

A recent webinar presentation from Industrial Information Resources (IIR) mentioned that – in times when capital expenditures are being reduced – companies need to focus more on process improvements, methods that reduce costs, and digitalization, AI/VR and predictive analytics.

By effectively utilizing the data generated throughout your plant, you can identify areas of operation or process adjustments that will be most likely to have a positive impact on improving quality, increasing efficiency and reducing waste.

One key way to make the most of existing capital equipment is to optimize your production processes using advanced data analytics tools and methodologies such as multivariate data analysis.

Multivariate data analysis uses regression models to summarize all of the individual data points from various operations into more unified data visualizations and charts. This can make it easier to see which parameters are having the most effect on outcomes, as well as understand how variables impact each other by looking at a single control chart.

[Watch this recorded webinar: Increasing the competitiveness of renewable chemicals production with process analytics]

Initiatives to Improve Your Production Processes

The key ways many manufacturers are looking to improve production outcomes, especially during this time of lean and critical operations, are:

  • Reduce unplanned downtime
  • Improve overall equipment efficiency (OEE)
  • Improve raw material utilizations
  • Enable operators to monitor one control chart with all relevant data
  • Be better able to identify defects
  • Model strategies and predict outcomes

Let’s take a more in-depth look at each of them.

By effectively utilizing the data generated throughout your plant, you can identify areas of operation or process adjustments that will be most likely to have a positive impact on improving quality, increasing efficiency and reducing waste.

Reduce Unplanned Downtime

You can use data coming from your instruments and analyzed in a multivariate way to manage the long-term operational health of production equipment, reduce unscheduled downtime, and prevent breakdowns. With a statistical analysis of all the factors that lead to wear and tear, you can get insights into the operational settings or processes most likely to extend the life of equipment or shorten downtimes between processes, and even implement predictive maintenance.

[Watch this recorded webinar: Predictive maintenance – monitoring and forecasting the state and performance of machines and equipment ]

Improve Overall Equipment Efficiency

Overall equipment efficiency (OEE) is the gold standard and a best practice for measuring manufacturing productivity. It provides an objective measurement for the percentage of manufacturing time that is truly productive. By measuring OEE and analyzing underlying losses and bottlenecks, you will gain important insights on how to systematically improve your manufacturing process.

Improve Raw Material Utilization

The right data analytics tool can help you assess the composition of chemicals, minerals and other raw materials to make sure they meet production requirements. This can help prevent counterfeit or impure raw materials from being used that could affect the quality of your final product or ruin batches. It can also help you find the right volume, temperature or composition of raw materials to optimize your process and produce the best quality product with the least waste.

Provide a Unified Control Chart

With typical process monitoring applications, your operators will have a number of different processes they need to watch and monitor for deviations. Combining multiple processes into a single control chart to monitor all processes and get alarms when a process starts deviating from the optimal path can be a huge benefit, especially when you are operating with a smaller than normal staff.

Better Identify Defects

With statistical process monitoring in place, you’ll be able to more quickly identify any defects in your products, materials, equipment or processes. Ultimately, that will mean an improvement in your overall production quality, reduction in process deviations and fewer ruined batches.

Model strategies and predict outcomes

The multivariate analysis model provides a basis for predicting quality parameters over time using regression analysis. It lets you predict the final critical quality attributes with a high degree of confidence. Chemical manufacturers can use advanced data analytics to compare and measure the effect of various production inputs, such as coolant pressure, temperature or carbon dioxide flow on yield – often finding surprising and unexpected dependencies that are impacting outpu

Data Analytics Tools to Improve Your Processes

SIMCA® is a data analytics software tool from Sartorius that is designed to support manufacturing operations like yours. SIMCA® helps you to analyze process variations, identify critical parameters and predict final product quality. In a few clicks, you get an overview of the process status.

You can complement the process improvement insights you gain from SIMCA® using the real-time data analytics tool: SIMCA®-online . SIMCA®-online helps you use data from all your processes in a proactive way with remote monitoring, predictive monitoring, fault detection, root cause analysis and automatic corrective recommendations.

One of the most effective ways to ensure your processes stay within their critical quality attributes is with real-time process monitoring. This allows you to see what’s happening with your processes as they are occurring. Being able to monitor processes in real time and take corrective action immediately will help minimize your operational costs while improving efficiency. That means more confidence in your production processes, and in your operations team, along with more consistent product quality.

See How It Works

To find out more about how SIMCA®-online can help with real-time monitoring and predictive optimization, sign up for a free demo.

Book a Free Demo

 

Topics: Real Time Process Monitoring, Statistical Process Control, Manufacturing Quality Control, Batch processes, Continuous Process Monitoring

Stefan Langner

Written by Stefan Langner

Stefan Langner is Market Manager for Process Industries (Food & Beverage and Chemical) markets at Sartorius Stedim Data Analytics.

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