Industrial Analytics
for Sugar Production

Unlock yield and optimize energy use across your entire sugar process

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Explore our solutions:

Ensure quality, food safety, and resource efficiency in every step of your process with TrendMiner.

Increasing quality and yield in  sugar production
Why Relevant:

Proper cooling and agitation are crucial for correct sugar crystallization. Inadequate supersaturation causes product defects or excess molasses, leading to reduced yield and inconsistent quality.

Why TrendMiner:

Balance production lines by calculating real-time usage rates of surrounding units from tank levels or flowrates. Determine if CIP can proceed safely or adjust extraction speed to maintain continuous, efficient operation without stoppages.

Optimizing energy consumption in sugar porduction
Why Relevant:

High energy consumption impacts efficiency and profitability. Optimizing steam, gas, and vacuum usage is essential to improve overall energy efficiency and significantly reduce operational costs.

Why TrendMiner:

Enable real-time monitoring and anomaly detection in boilers and extraction units to detect issues early. Leverage dashboards and ContextHub historical data to identify deviations, correct them fast, and prevent process failures.

Reducing waste and losses in sugar production
Why Relevant:

Inefficient extraction leaves excess sugar in the pulp, lowering yield and increasing waste. Precise moisture control is essential to enhance recovery and overall process efficiency.

TrendMiner’s Solution:

We can diagnose and analyze pasteurization issues, reducing downtime and maintaining the min required temperature. For CIP processes, It also enables the creation of a reference "ideal CIP" pattern to ensure contamination-free operations.

Saving time in  sugar production
Why Relevant:

Dull blades and uneven cossettes hinder extraction efficiency, increasing processing time and reducing overall yield and performance.

TrendMiner’s Solution:

Analyze correlations between blade dullness and uptime to compare mill performance. Monitor dullness trends to identify how blade wear impacts cutting quality, sieve clogging, and overall extraction efficiency.

free guide

5 Hidden Opportunities to Boost Sugar Yield, Cut Energy Costs, and Stabilize Quality

A practical guide for process engineers and plant managers in sugar production.

A platform where human intelligence meets advanced analytics

Fast & intuitive

Access and explore data effortlessly with a user-friendly platform for the whole team.

Advanced analytics

Empower everyone to generate insights with powerful analytics to drive data driven decisions.

Reporting & collaboration

Share insights, collaborate across teams, and create personalized dashboards to track progress.

Monitoring & alerts

Keep your plant and assets running smoothly with 24/7 monitoring and instant alerts.

Data contextualization

Transform time-series data into meaningful events in your enhanced data layer.

Ready to transform your industry?

Discover powerful insights and make data-driven decisions.

Latest resources

Explore Our Newest Content to Maximize Your Operational Efficiency

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Automating and Comparing Regeneration Cycles Across Sites

Maximize throughput and resource efficiency through effective process order management, inventory alignment, and performance monitoring (OEE), ultimately to minimize waste and optimize operations.

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Optimizing Sugar Beet Pulp Dry Mass with Smart Dilution Reporting

Maximize throughput and resource efficiency through effective process order management, inventory alignment, and performance monitoring (OEE), ultimately to minimize waste and optimize operations.

Use case

Stabilizing Sugar Crystallization for Quality & Efficiency

Maximize throughput and resource efficiency through effective process order management, inventory alignment, and performance monitoring (OEE), ultimately to minimize waste and optimize operations.

process engineers working with industrial analytics software in a factory

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Discover powerful insights and make data-driven decisions with TrendMiner's time-series analysis platform.