by MarsdenMarketing | Nov 14, 2024 | Case Study, Paper & Pulp
| Solenis OPTIX™ Applied Intelligence is our exclusive partner in modernizing the pulp and paper industry, leveraging cutting-edge AI-driven autonomous solutions to optimize processes, reduce waste, and achieve unprecedented operational efficiency. |
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Company
North American Board Producer
Challenge: Inefficient Chemistry Dosage Scheme for Wet-Strength Chemistry
A North American board producer wanted to reduce raw material consumption while optimizing wet tensile quality. The mill employed an inefficient dosage scheme for wet-strength chemistry. The dynamically changing nature of continuous manufacturing and the periodic reel-to-reel quality measurement environment of tissue-making presented operators with a challenge for manual optimization.
Solution: Fine-Tuning Chemistry Dosage With AI
The board producer implemented Solenis OPTIX™ Applied Intelligence, powered by ProcessMiner. A machine-learning, predictive analytics platform with autonomous control capabilities, OPTIX delivers a virtual measure of wet tensile quality in real time and uses machine learning capabilities to remain robust and accurate in the face of changing process conditions. Using artificial intelligence (AI) to make data-driven process adjustments, the wet strength chemistry dosage is finely tuned to drive wet tensile quality to the target.
Results: Achieving Immediate Cost Savings
Over a two-month period of using OPTIX’s autonomous control, the mill realized a 14% reduction in wet strength chemistry usage across all grades. The autonomous control algorithms adjusted the wet strength chemistry dosage up or down to ensure target adherence of the wet tensile quality parameter. This unprecedented, AI-driven autonomous control optimized wet tensile quality by reducing variation by 15%, all while avoiding off-quality production. This improvement in target adherence allowed the mill to then lower wet tensile targets to drive further optimization.
Disclaimer: This case study highlights the strategic partnership between ProcessMiner and Solenis in pulp and paper manufacturing. ProcessMiner’s AI-driven solutions also extend beyond the pulp and paper industry, offering optimization across multiple industries.
by MarsdenMarketing | Nov 14, 2024 | Case Study, Paper & Pulp
| Solenis OPTIX™ Applied Intelligence is our exclusive partner in modernizing the pulp and paper industry, leveraging cutting-edge AI-driven autonomous solutions to optimize processes, reduce waste, and achieve unprecedented operational efficiency. |
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Company
Board Mill
Challenge: Reactive Control Leads to Kymene Overdose
A board mill was interested in optimizing Kymene consumption while maintaining wet tear quality. The dynamically changing nature of paper manufacturing presented operators with a challenge for Kymene optimization. Additionally, delayed or erroneous quality tests caused mill operators to reactively control and, at times, overdose Kymene. Frequent turnover or inexperienced operators presented additional challenges for mill operations.
Solution: Getting Kymene Dosage Under Control With ProcessMiner
The board mill implemented Solenis OPTIX™ Applied Intelligence, powered by ProcessMiner. The platform generates real-time, virtual measures of wet tears for pulp and paper manufacturers and incorporates an AI-driven control loop to optimize the associated chemistry program. Machine learning is also used to ensure the quality measure remains accurate.
Results: Achieving Immediate Cost Savings
Complete autonomous chemistry control: OPTIX continuously optimizes the control of wet strength chemistry for all wet tear grades. AI-autonomous control reduced dry-end test variation and improved target adherence at lower overall chemical spend.
Optimized chemistry dosage: Machine learning enables OPTIX to drive Kymene to the dose required to maintain wet strength quality, even during process changes. Mill operators no longer need to make knee-jerk reactions to quality tests, while management takes comfort in knowing OPTIX is administering the correct amount of Kymene for the process. By allowing the platform to make decisions about Kymene dosage, the mill optimized dosage and reduced lab variation for each grade.
Figure 1 highlights the variation reduction and mean shift OPTIX was able to drive. For the grade highlighted, OPTIX improved target adherence by 38% and reduced variation by over 28%. This improved control allowed for a 15% optimization in chemistry, as seen in Figure 2.
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Focus on quality: Customized algorithms allow OPTIX autonomous control to drive quality to the target. Since its installation, the platform has improved or maintained wet strength target adherence while producing no additional rejects. Allowing OPTIX to reduce variation in chemistry systems yields benefits beyond chemistry optimization. The improved wet tear quality has reduced variation in dry tears and improved basis weight control. As variation reduces across the asset, system noise is eliminated, and overall machine efficiency improves.
Disclaimer: This case study highlights the strategic partnership between ProcessMiner and Solenis in pulp and paper continuous manufacturing as strategic partners. ProcessMiner’s AI-driven solutions also extend beyond the pulp and paper industry, offering optimization in various sectors.
by MarsdenMarketing | Nov 14, 2024 | Case Study, Paper & Pulp
| Solenis OPTIX™ Applied Intelligence is our exclusive partner in modernizing the pulp and paper industry, leveraging cutting-edge AI-driven autonomous solutions to optimize processes, reduce waste, and achieve unprecedented operational efficiency. |
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Company
North American Tissue and Towel Producer
Challenge: Inefficient Chemistry Dosage Scheme
A North American tissue and towel producer was interested in reducing raw material consumption while optimizing wet tensile quality. The mill employed an inefficient dosage scheme for wet-strength chemistry. The dynamically changing nature of continuous manufacturing and the periodic reel-to-reel quality measurement environment of tissue-making presented operators with a challenge for manual optimization. This prompted the mill to search for an autonomous control solution built for the pulp and paper industry.
Solution: Optimizing Chemistry Dosage With Solenis OPTIX™ Applied Intelligence, Powered by ProcessMiner
The mill implemented OPTIX an AI and ML, predictive analytics platform with autonomous control capabilities. OPTIX generates a virtual measure of wet tensile quality in real time and uses machine learning capabilities to remain robust and accurate in the face of changing machine conditions. Leveraging artificial intelligence (AI) to make data-driven process adjustments, the wet strength chemistry dosage is finely tuned to drive wet tensile quality to the target.
Results: Achieving Immediate Cost Savings
Over a six-month period of using OPTIX, the mill achieved a 25% reduction in wet strength chemistry usage through autonomous control.
The autonomous control algorithms adjusted the wet strength chemistry dosage up or down to ensure target adherence to the wet tensile quality parameter. OPTIX optimized wet tensile quality by reducing quality variation by 23% and increasing target adherence by 63% all while avoiding off-quality production.
Disclaimer: This case study highlights the strategic partnership between ProcessMiner and Solenis in pulp and paper continuous manufacturing as strategic partners. ProcessMiner’s AI-driven solutions also extend beyond the pulp and paper industry, offering optimization in various sectors.
by MarsdenMarketing | Oct 17, 2024 | Case Study, Paper & Pulp
| Solenis OPTIX™ Applied Intelligence is our exclusive partner in modernizing the pulp and paper industry, leveraging cutting-edge AI-driven autonomous solutions to optimize processes, reduce waste, and achieve unprecedented operational efficiency. |
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Company
North American Virgin Coated Paper Board Mill
Challenge: Inefficient Quality Tests Result in Reactive Control and Chemistry Overdose
A North American virgin-coated paper board mill was interested in optimizing wet strength resin (WSR) consumption while maintaining wet tear quality. The dynamically changing nature of paper manufacturing presented operators with a challenge for manual WSR optimization. Additionally, delayed or erroneous quality tests caused mill operators to reactively control and overdose WSR, prompting the search for an autonomous chemistry optimization solution designed for the pulp and paper industry.
Solution: Incorporating AI-Driven Control Loop
The mill implemented Solenis OPTIX™ Applied Intelligence, powered by ProcessMiner. It’s a machine-learning, predictive analytics platform with autonomous control capabilities. The solution generated a real-time, virtual measure of wet tears and incorporated an AI-driven control loop to optimize the associated chemistry program. Machine learning was also used to ensure the quality measure remains accurate.
Results: Achieving Immediate Cost Savings
Complete autonomous chemistry control: OPTIX optimally controls wet strength chemistry for all the customer’s grades. Mill operators have embraced AI-autonomous control as a new tool to control wet strength chemistry.
Optimized chemistry dosage: Machine learning enables OPTIX to drive the WSR to the dose required to maintain wet strength quality, even during process changes. Mill operators no longer need to make knee-jerk reactions to quality tests while management takes comfort in knowing OPTIX is administering the correct amount of WSR for the process.
By allowing OPTIX to make decisions about WSR dosage, the mill can optimize dosage for each grade. An average dosage reduction of 18% across the heavy-weight grades was achieved by using OPTIX.
Focus on quality: Customized algorithms allow OPTIX autonomous control to drive quality to target specs. Since its installation, OPTIX has improved or maintained wet strength target adherence while producing no additional rejects. During upset conditions on a few light weights, OPTIX proactively increased WSR dosage to ensure wet strength quality persisted. The improved quality compliance has satisfied the quality control department and eliminated petitions from the quality manager.
Disclaimer: This case study highlights the strategic partnership between ProcessMiner and Solenis in pulp and paper continuous manufacturing as strategic partners. ProcessMiner’s AI-driven solutions also extend beyond the pulp and paper industry, offering optimization in various sectors.
by Team ProcessMiner | Mar 5, 2024 | Case Study
Wastewater treatment plants (WWTPs) are facing growing pressure to become more efficient and environmentally friendly. WWTPs adopting ProcessMiner’s advanced AI technology are making great progress in optimizing their operations. By taking advantage of ProcessMiner’s modern wastewater treatment solutions, WWTPs are able to automate critical activities, use resources more efficiently, and reduce their environmental impact. AI also enables the optimization of chemistry and energy consumption in clarifiers, aeration basins, anaerobic /aerobic digesters, and the sludge dewatering process.
Addressing the Challenges WWTPs Face
WWTP operators and environmental engineers often grapple with significant challenges in managing operational efficiency and meeting effluent quality limits. Challenges also include:
- Handling increasing amounts of contaminants
- Reducing the environmental impact of sludge disposal
- Optimizing the use of chemicals
- Using energy more efficiently
- Executing corrective actions in a timely manner
- Reducing human error
The ability to address these obstacles directly contributes to operational cost management and environmental impact, which can threaten economic viability and environmental responsibility. While there are several strategies to help address these challenges, one key strategy is automating the optimization of polymer dosing, which is critical to the sludge dewatering process.
The execution of an optimized polymer dosing strategy can lead to significant improvements. Today, we know up to 40% of the cost of water treatment is associated with sludge dewatering. Therefore, the ability to dose the optimum amount of polymer supports the production of a drier sludge cake, reducing the cost of chemicals and increasing plant efficiency as centrifuges, screw presses, and belt presses operate efficiently and optimally.
How ProcessMiner Optimizes the Wastewater Treatment Process
ProcessMiner’s turnkey solution is specifically designed to automate the sludge dewatering process.
By combining the power of AI with intelligent sensing meters placed at critical points in the dewatering process, the solution works by monitoring constantly changing sludge conditions and precisely adjusting polymer dosage to consistently produce a drier sludge cake.
This transformative approach to wastewater treatment supports timely and precise control setting adjustments to improve the efficiency and effectiveness of the water treatment process. In addition, since the platform runs autonomously, it frees up plant operators to focus on other critical plant activities. Finally, because ProcessMiner is adaptive and self-learning, it requires no staffing of data engineers or scientists to maintain the solution as dynamic conditions in the plant evolve.
The end result? Optimized polymer dosing and reduced energy consumption, all while resulting in lower sludge cake transportation and disposal costs. The integration of traditional water treatment processes and plant domain expertise with this level of advanced technology ensures that WWTPs operate at optimal efficiency with the lowest possible environmental impact.
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by Team ProcessMiner | Feb 11, 2021 | Case Study, Plastics
Company
F500 Plastic Injection Molding Manufacturer
Challenge: Dealing With Unusually High Defect Rates
A leading plastic injection molding manufacturer was experiencing high defect rates in a medical specialty plastic vial product. The manufacturer solicited the help of ProcessMiner to identify and mitigate the root causes associated with the defect rates.
Solution: Using AI To Find Out Scrap Rate Root Cause
Recognizing the potential to leverage AI for plastics manufacturing process optimization, together the manufacturer and ProcessMiner collaborated on a pilot project. The primary goal was to assess if ProcessMiner’s AI platform could effectively monitor, predict, and prevent defects that were increasing scrap rates in excess of 20% above the company average.
The project began with an extensive offline data analysis to evaluate the data quality accurately from the pilot machine. This critical step enabled a detailed understanding of essential manufacturing quality parameters and control elements.
Within the first 90 days, ProcessMiner’s AI platform analyzed data from more than 300 sensors on the injection molding machine. This analysis pinpointed two specific defect types responsible for a majority of the scrap in the specialty bottle production.
Using these insights, ProcessMiner’s AI platform provided targeted recommendations for adjusting key parameters such as temperature and pressure settings. These recommendations were crucial for refining production quality.
Results: 6-Figure Savings and Improved Scrap Rate
Implementing ProcessMiner’s AI-driven recommendations reduced scrap rates by 25%, significantly enhancing operational efficiency. The pilot also equipped the manufacturer with advanced tools for real-time monitoring and automated corrective action. These tools facilitated proactive adjustments through prescriptive parameter control recommendations automatically transmitted to the plastic injection machine control systems.
ProcessMiner’s introduction of autonomous control that allowed for automatic adjustments to production set points via IoT technology. This closed-loop system and autonomous control platform streamlined operations, consistently reduced defects, and maximized productivity, resulting in a higher first-pass yield.
Throughout the pilot, ProcessMiner’s AI platform autonomously adjusted the plastic injection molding machine settings at the start of each manufacturing shift. This consistency significantly reduced production variability and increased stability.
By autonomously maintaining optimal machine settings, the platform helped create a more predictable and efficient manufacturing environment, ultimately yielding annualized six-figure operational savings for the company.
