Data Analytics for the Chemical Industry
Optimize Raw Material Selection, Processes and Control
In industries that manufacture complex chemicals, finding ways to optimize quality, reduce waste and improve inefficiencies often hinges on selecting the right chemical compounds or optimizing production processes. Data analytics software can improve this effort at every stage: research, development and manufacturing.
The Umetrics® Suite of data analytics tools can help you find alternative compounds that meet complex requirements, decrease raw material usage or enable more cost-effective, sustainable manufacturing procedures.
The Umetrics® software can also help you monitor production in real-time, particularly batch processes, preventing waste and quality concerns.
Select the Right Raw Materials
When it comes to evaluating the chemical properties of a compound, data analytics streamlines the process. MODDE® Design of Experiments (DOE) software lets you select a small set of diverse and representative compounds for testing. This allows you to predict the properties of untested compounds based on the properties tested and reduces the number of experiments you need to do to find the most promising product for your purposes, saving time and money.
Optimize Production Processes
The SIMCA® Multivariate Data Analysis software uses principal component analysis (PCA) and other methods like partial least squares (PLS) or our proprietary orthogonal PLS (OPLS) to visualize data, providing reliable, data-backed guidance to adjust production processes in order to use raw materials more efficiently, increase output, maintain quality, and reduce wear and tear on equipment.
Keep Processes Under Control
One of the most effective ways to ensure your processes stay under control and meet their critical quality attributes is with real-time process monitoring. Being able to take corrective action immediately helps reduce waste (lost batches) and increase yield. With SIMCA®-online, you’ll gain confidence in your production processes and achieve more consistent product quality.
Using KPIs to Select Compounds
Rather than relying on trial and error or published descriptions, chemists can use their own process data to understand what factors matter most when selecting compounds. Key performance indicators (KPIs) can be calculated from data found in batch records or representative laboratory experiments.
Some common KPIs of organic process chemistry may include:
- process mass intensity (PMI)
- costs per kg final product
- overall yield
- step count
- longest linear sequence
- volume-time output
- similarity of other chemical properties
Soft factors, such as process robustness – which are typically hard to quantify but are important for judging the quality of a chemical process – can be evaluated with data analytics as well.
The ROI for data analytics software is high: delivering reductions in waste and errors, advanced selection of chemicals and optimized processes and quality.
Optimize and Control Processes
Data analytics software helps you optimize your processes and keep them under control during manufacturing.
- Reduce unplanned downtime
- Improve overall equipment efficiency (OEE)
- Improve raw material utilizations
- Monitor one control chart
- Identify defects quickly
- Model strategies and predict outcomes
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.
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, composition or any other relevant property of raw materials to optimize your process and produce the best quality product with the least waste.
Get 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. Drill-down capabilities will allow you to identify root causes for deviations immediately.
Identify Defects Quickly
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, and many more on yield – often finding surprising and unexpected dependencies that are impacting output
Support for Renewable Chemicals
The renewable chemicals market is seeing extraordinary growth. Renewable, or green chemicals, are chemical products made from renewable resources, such as biomass feedstock instead of petroleum feedstock. Data analytics software can be key in migrating processes toward renewable chemicals.
- Find optimization opportunities that drive cost down and yield up
- Optimize utilization of 2nd generation feedstock (e.g. wood chip, straw, coffee grounds, etc.)
- Gain process understanding and find the most influential variables
- Added benefit: the investment is in software alone
Batch Modeling Provides the Solution
Historical data that characterizes your product or process is useful in creating an optimized process. This includes:
- yield and quality parameters
- start and end times
- pressure, temperature, flow rate and other physical units
- any other relevant variable recorded during the batch run
Visualize Variable Over Time
Plotting the multivariate variable t1 against time results in a trajectory for every historical batch, allowing to easily distinguish between good and bad batches. Good batches are within the two dotted red lines. The closer to the center line the trajectory is the better the results will be.