In previous blog posts, we discussed why the process industry should care about Artificial Intelligence (AI) and how it is revolutionizing the chemical industry as a whole. In this post, we will focus on the glass industry—a cornerstone of modern manufacturing—and explore how this sector is embracing the transformative power of AI to address longstanding challenges and unlock new opportunities. From energy optimization to quality assurance, AI is revolutionizing glass production processes, making them more efficient, sustainable, and cost-effective.
We'll explore several use cases where AI, particularly leveraging Intelecy's no-code Industrial AI platform, can drive significant impact. With its intuitive no-code user interface, process and chemical engineers can independently build and operationalize machine learning models without requiring support from IT teams or data scientists. Let’s take a closer look at the potential use cases these models can address.
1. Energy Optimization and Melting Efficiency
Challenge:
Glass furnaces operate at extremely high temperatures to melt raw materials, consuming substantial amounts of energy. Optimizing temperature control, fuel consumption, and raw material feed rates is crucial for consistent melting and energy reduction.
Solution:
The machine learning model monitors furnace temperature, fuel input, and glass composition in real-time. By implementing a closed-loop system, AI predictions can adjust furnace parameters dynamically to ensure consistent melting while reducing energy consumption by 3%. The model can also predict when furnace refractory materials will degrade, allowing for proactive maintenance and minimizing downtime.
Benefits:
- Reduced energy costs
- Improved furnace lifespan
- Enhanced operational efficiency
2. Temperature Control and Product Consistency
Challenge:
Forehearths condition molten glass before it reaches forming equipment. Maintaining a uniform temperature profile along the forehearth is essential to ensure consistent viscosity and prevent defects like bubbles and inclusions.
Solution:
The machine learning model monitors temperatures along the forehearth and dynamically adjusts heating zones to maintain uniform viscosity in the molten glass. This proactive control reduces product defects by 3%, resulting in higher-quality glass products.
Benefits:
- Improved product quality
- Reduced defect rates
- Enhanced process stability
3. Process Optimization and Defect Detection
Challenge:
Individual Section (IS) machines form molten glass into shapes like bottles and jars. Variations in forming parameters such as temperature, pressure, and timing can lead to defects like cracks or uneven thickness.
Solution:
The machine learning model tracks real-time data from IS machines, including mold temperature and pressure. It detects slight variations in forming pressure that could cause thin spots in the glass. By adjusting forming parameters through closed-loop functionalities, the model reduces defect rates by 4% and consequently improves production throughput.
Benefits:
- Increased production efficiency
- Lower defect and rejection rates
- Optimized machine performance
4. Stress Control and Cooling Optimization
Challenge:
The annealing lehr gradually cools glass to relieve internal stresses. Uneven or improper cooling can weaken the glass, leading to increased breakage.
Solution:
The machine learning model analyzes temperature and cooling patterns within the annealing lehr. It detects uneven cooling rates across the width of the glass sheet and adjusts air circulation fans to ensure uniform cooling. This optimization reduces internal stresses and increases product strength by 3%.
Benefits:
- Stronger, more durable glass products
- Reduced breakage rates
- Consistent product quality
5. Raw Material Quality Prediction and Mixing Optimization
Challenge:
The batch house mixes raw materials such as sand, soda ash, and limestone. Variations in raw material quality can affect glass composition, leading to waste and inconsistent products.
Solution:
The machine learning model monitors raw material quality parameters like sand grain size and purity. It adjusts mixing ratios in real-time to maintain the desired glass composition. This results in a 3% reduction in material waste and improves the consistency of the glass batch, reducing the number of rejected products.
Benefits:
- Reduced material waste
- Consistent glass composition
- Lower production costs
Conclusion: Embracing the Future with AI
The integration of AI into the glass industry is more than a technological upgrade; it's a strategic move towards increased efficiency, quality, and sustainability. From energy optimization to defect detection, AI can provide insights and automation at every stage of glass production. The result is a more agile, competitive, and environmentally friendly industry.
By leveraging Intelecy's No-code Industrial AI solution, subject matter experts like chemical and process engineers can build the machine learning models and solve challenges without the need for extensive programming expertise or support from IT.
Ready to Transform Your Glass Manufacturing Operations?
At Intelecy, we're committed to supporting glass industry clients in optimizing their production with AI. Learn more about our No-code Industrial AI solution and get in touch to book a demo or discuss how we can help you achieve new heights in efficiency and quality.
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