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About Sitech
Sitech Services, the number-one technology partner to maximize results out of each installation and plant – in terms of both performance and returns.
They help companies in the process industry, the chemical industry and the energy sector to grow and develop. This by support them with their unique services and the latest technologies, using expertise, innovation and extensive digitization as tools to take the lead.
Here is what they say
Marc Pijpers is Principal Process Control & Optimization Consultant at Sitech and works with TrendMiner for more than five years. Marc explains about their journey, the value and next steps in using TrendMiner for all their customers at Chemelot.
Pieter Kerremans, Process Engineer at Sitech that also works as contractor for DSM for continuous improvement projects, explans how TrendMiner helps in root cause analysis.
Sitech Journey & Business Challenges
The need for proper data analytics was stressed when the Sitech Asset Health Center was founded, adding new services to the portfolio.
The objective: zero surprises and no unplanned downtime of assets, which meant Sitech needs to be able to predict the performance and conditions of assets at all times.
This is when Marc Pijpers, Principal Process Control Engineer at Sitech asked the question: how can we overcome these challenges by exploiting the possibilities of self-service analytics?
Solution & Implementation
Translating big data analytics challenges into a proper business case was crucial in order for Sitech to allocate sufficient time and money. Finding a supplier that is capable to provide the right software (proof of product), as well as help adapt an analytics mindset and bring added value (proof of concept) is important.
TrendMiner enables Sitech to perform descriptive, diagnostic and predictive analytics. Which means it helps process engineers to gain insight into what happened (descriptive), why and how it happened (diagnostic) and how the situation can be prevented or ensured to happen again in the future (predictive).
Sitech uses TrendMiner for:
- Comparison of (similar) situations, to find out which events result in (un)desired situations
- Root cause analysis sessions, which immediately answers questions that traditionally were put on an action list
- Data cleaning for APC and 6sigma, to create subsets of relevant data
- Automatic notifications, eliminating repetitive tasks by automatically monitoring important processes.
Use Case 1
Diagnostic analytics: 5%+ revenue increase, representing $2.4m/year
The Haber-Bosch nitrogen-fixation reaction is used for over 100 years to make ammonia and other nitrogencontaining compounds from nitrogen and hydrogen. Although perceived as a fully optimised production process, carbon dioxide peaks were found in the washing column. Previous data analysis projects failed to find the root cause, but TrendMiner’s self-service industrial analytics software helped Sitech to search and compare peak periods with normal operation periods. Also, the team performed layer comparison which helped identify which processes influenced variables. With help of these findings, Sitech was able to realize a stable operation, which resulted in increased production, with a 5% revenue increase – representing 2.4 million dollar per year – as a result.
Use Case 2
Descriptive analytics: gaining 5 years of valuable data insight in minutes rather than weeks
In another case, process engineers of Sitech experienced that thermal stress of a reactor was too high due to repetitive fast cooling and heating. With TrendMiner, the team set up a way to monitor the amount of times the problem occured in order to gain insight into how many thermal cycles result in a failure. Also, the software notified the team when the reactor had been thermally stressed. In a matter of minutes, Sitech’s team was able to look back 5 years in time and export valuable search results to perform lifespan residue analysis.
Use Case 3
Contextualize asset performance with process data: 1%+ overall revenue increase of the entire production line
One example where asset performance is directly related to process behavior is the fouling of heat exchangers. In a reactor with subsequent heating and cooling phases, the controlled cooling phase is the most time-consuming, and it is almost impossible to monitor fouling when the reactor is used for different product grades and a different recipe is required for each grade. Fouling of heat exchangers increases the cooling time, but scheduling maintenance too early leads to unwarranted downtime and scheduling too late leads to degraded performance, increased energy consumption and potential risks.
In the instance of the production of a polymer, a monitor was set up to look at cooling the times of their most highly produced products. If the duration of the cooling phase starts to increase, a warning is sent to the engineers who can then schedule timely maintenance. The gained benefits are extended asset availability, predictive maintenance leading to operational and maintenance cost reduction and reduction of safety risk. The overall impact was 1%+ overall revenue increase of the entire production line.
Use Case 4
Diagnostic analytics: improving process insight and reducing manual labor costs
In this particular case, the Sitech team was discovering certain peaks in product flows, causing unwanted saturation of sensors. Manual correction was inevitable for KPI reporting. Influence factor analysis was performed on peaks, identifying the root cause and insight into how highly correlated events are, as well as showing the significance of the event occurring. This helped the Sitech team to improve process insight, reduce manual labor costs and improve process stability.
Success Story & Real Life Use Cases
Enabling predictive maintenance with industrial analytics
In this webinar, Marc Pijpers of Sitech shares his experience of using advanced industrial analytics to contextualize asset performance with process data, and illustrates the benefits with 3 actual use cases.
Blog Post
Self-service industrial analytics – Five reasons to choose TrendMiner
Self-service industrial analytics delivers value in a lot of different areas. Here are the top reasons customers gave for selecting our software this year, and why you may want to reconsider your analytics strategy.
Continue reading…
Sitech
Chemical
Asset Performance Management
Asset Optimization and Monitoring
Process Engineer
Maintenance Engineer
Reliability Engineer
Automation Engineer
About Sitech
Sitech Services, the number-one technology partner to maximize results out of each installation and plant – in terms of both performance and returns.
They help companies in the process industry, the chemical industry and the energy sector to grow and develop. This by support them with their unique services and the latest technologies, using expertise, innovation and extensive digitization as tools to take the lead.
Here is what they say
Marc Pijpers is Principal Process Control & Optimization Consultant at Sitech and works with TrendMiner for more than five years. Marc explains about their journey, the value and next steps in using TrendMiner for all their customers at Chemelot.
Pieter Kerremans, Process Engineer at Sitech that also works as contractor for DSM for continuous improvement projects, explans how TrendMiner helps in root cause analysis.
Sitech Journey & Business Challenges
The need for proper data analytics was stressed when the Sitech Asset Health Center was founded, adding new services to the portfolio.
The objective: zero surprises and no unplanned downtime of assets, which meant Sitech needs to be able to predict the performance and conditions of assets at all times.
This is when Marc Pijpers, Principal Process Control Engineer at Sitech asked the question: how can we overcome these challenges by exploiting the possibilities of self-service analytics?
Solution & Implementation
Translating big data analytics challenges into a proper business case was crucial in order for Sitech to allocate sufficient time and money. Finding a supplier that is capable to provide the right software (proof of product), as well as help adapt an analytics mindset and bring added value (proof of concept) is important.
TrendMiner enables Sitech to perform descriptive, diagnostic and predictive analytics. Which means it helps process engineers to gain insight into what happened (descriptive), why and how it happened (diagnostic) and how the situation can be prevented or ensured to happen again in the future (predictive).
Sitech uses TrendMiner for:
- Comparison of (similar) situations, to find out which events result in (un)desired situations
- Root cause analysis sessions, which immediately answers questions that traditionally were put on an action list
- Data cleaning for APC and 6sigma, to create subsets of relevant data
- Automatic notifications, eliminating repetitive tasks by automatically monitoring important processes.
Use Case 1
Diagnostic analytics: 5%+ revenue increase, representing $2.4m/year
The Haber-Bosch nitrogen-fixation reaction is used for over 100 years to make ammonia and other nitrogencontaining compounds from nitrogen and hydrogen. Although perceived as a fully optimised production process, carbon dioxide peaks were found in the washing column. Previous data analysis projects failed to find the root cause, but TrendMiner’s self-service industrial analytics software helped Sitech to search and compare peak periods with normal operation periods. Also, the team performed layer comparison which helped identify which processes influenced variables. With help of these findings, Sitech was able to realize a stable operation, which resulted in increased production, with a 5% revenue increase – representing 2.4 million dollar per year – as a result.
Use Case 2
Descriptive analytics: gaining 5 years of valuable data insight in minutes rather than weeks
In another case, process engineers of Sitech experienced that thermal stress of a reactor was too high due to repetitive fast cooling and heating. With TrendMiner, the team set up a way to monitor the amount of times the problem occured in order to gain insight into how many thermal cycles result in a failure. Also, the software notified the team when the reactor had been thermally stressed. In a matter of minutes, Sitech’s team was able to look back 5 years in time and export valuable search results to perform lifespan residue analysis.
Use Case 3
Contextualize asset performance with process data: 1%+ overall revenue increase of the entire production line
One example where asset performance is directly related to process behavior is the fouling of heat exchangers. In a reactor with subsequent heating and cooling phases, the controlled cooling phase is the most time-consuming, and it is almost impossible to monitor fouling when the reactor is used for different product grades and a different recipe is required for each grade. Fouling of heat exchangers increases the cooling time, but scheduling maintenance too early leads to unwarranted downtime and scheduling too late leads to degraded performance, increased energy consumption and potential risks.
In the instance of the production of a polymer, a monitor was set up to look at cooling the times of their most highly produced products. If the duration of the cooling phase starts to increase, a warning is sent to the engineers who can then schedule timely maintenance. The gained benefits are extended asset availability, predictive maintenance leading to operational and maintenance cost reduction and reduction of safety risk. The overall impact was 1%+ overall revenue increase of the entire production line.
Use Case 4
Diagnostic analytics: improving process insight and reducing manual labor costs
In this particular case, the Sitech team was discovering certain peaks in product flows, causing unwanted saturation of sensors. Manual correction was inevitable for KPI reporting. Influence factor analysis was performed on peaks, identifying the root cause and insight into how highly correlated events are, as well as showing the significance of the event occurring. This helped the Sitech team to improve process insight, reduce manual labor costs and improve process stability.
Success Story & Real Life Use Cases
Enabling predictive maintenance with industrial analytics
In this webinar, Marc Pijpers of Sitech shares his experience of using advanced industrial analytics to contextualize asset performance with process data, and illustrates the benefits with 3 actual use cases.
Blog Post
Self-service industrial analytics – Five reasons to choose TrendMiner
Self-service industrial analytics delivers value in a lot of different areas. Here are the top reasons customers gave for selecting our software this year, and why you may want to reconsider your analytics strategy.
Continue reading…
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