Ayo Biomass | Koidra’s Intelligent Automation for Improved Efficiency

Executive Summary

In 2024, Koidra partnered with Ayo Biomass to optimize operations through an advanced, data-driven approach. Ayo Biomass faced challenges related to managing diverse feedstock moisture levels, particle sizes, and operational inefficiencies. DataPilot came first and put the plant’s data in one place. KoPilot then ran the dryers in a closed loop, moving their setpoints against the moisture actually arriving, and dryer operational efficiency rose by up to 20% while it ran. The gain did not hold in every period, and the reason was upstream of the controller: instruments that read wrong and readings with no agreed limit. In 2026 Ayo is renewing its dryer control, starting with Predix advice that the operators act on, with Koidra CMMS running maintenance at both plants.

Customer Overview

Ayo Biomass a leading wood pellet producer based in Vietnam, has been supplying sustainably sourced wood pellets to the Asian green energy market since 2017. By 2024, Ayo Biomass sought to remain competitive by optimizing its production processes, improving resource utilization, and reducing operational costs. Koidra’s solutions, DataPilot and KoPilot, were selected to enhance data insights and optimize key operational areas throughout the plant.

Wood chips and sawdust waiting for the dryer. The moisture in this pile is the first number the process has to absorb.

Ayo Biomass’s Plant 1 has already had a Drying line. Working with Koidra, Ayo Biomass has 2 requests:

• Improve the performance of their existing Drying line

• Provide a complete automation solution for their new Drying line

Challenges and Solutions

Ayo Biomass faced two main challenges:

• Data Collection and Insights: The company needed more robust, centralized data collection and analysis capabilities. While basic production metrics were available, Ayo Biomass lacked the ability to record, analyze, and act on historical data, limiting its ability to optimize operations in real-time and over time.

• Production and Energy Optimization: Ayo Biomass required a solution to optimize production throughput and energy consumption while minimizing manual intervention. Their existing systems were inefficient and lacked the flexibility necessary to adjust dynamically to changing operational conditions. Koidra’s KoPilot was introduced to optimize these processes by providing real-time control and transparency into operational decisions.

Phase 1: Implement DataPilot for Data Collection, Monitoring, and Remote Management

Challenge

Before DataPilot, Ayo Biomass struggled to effectively collect, analyze, and utilize operational data for production optimization. Although basic metrics like feedstock moisture levels, pelletizing capacity, and energy consumption were available, the company lacked the ability to record and analyze historical data or remotely access insights. This limited their ability to track performance trends over time, respond quickly to operational issues, and make data-driven decisions.

Solution

Koidra implemented DataPilot, a platform designed specifically to address the operational needs of manufacturing companies. By centralizing and streamlining data collection across the entire plant, DataPilot enabled Ayo Biomass to go beyond basic metrics and achieve deeper insights through historical data analysis, real-time monitoring, and remote access.

• Agility in Data Analysis: DataPilot enabled rapid data analysis, eliminating bottlenecks by removing the need for IT or database administrators. This agility allowed shop-floor personnel and managers to quickly respond to emerging trends, identify inefficiencies, and make informed decisions that enhanced operational performance.

Daily outputs from 8 pettetization machines in August, 2024.

• Remote Monitoring and Historical Data: DataPilot allowed Ayo’s team to remotely monitor plant operations and access historical data. This capability enabled the team to reorganize data for trend analysis and optimize decision-making over time. With the ability to monitor performance from any location, Ayo Biomass could identify issues faster and implement corrective actions, leading to improved oversight and operational efficiency.

Moisture levels before and after drying are remotely monitored. Pellet moisture after drying is also recorded.

• Smart Alarms: The platform also introduced AI-driven intelligent alerts, designed to minimize false positives and ensure that only critical issues were flagged. This rule-based and AI-driven system helped reduce disruptions while maintaining confidence in automated processes.

Examples of Smart Alarms in DataPilot.

• Condensation caught before it built up: When the gas leaving a dryer cools toward its dew point, moisture can condense and carry dust and material onto the equipment downstream, and over time that buildup affects airflow, balance and reliability. DataPilot put the dew point margin beside the dryer outlet temperature, so a margin nobody had been comparing became a number the team could set an alarm on.

Where an alarm ends up. Sixty-three jobs waiting on Koidra’s demo pellet plant rather than Ayo’s, and one of them opened: the asset it belongs to, where that asset sits, whether the maintenance plan raised it or a person did, and the procedure that travels with it.

Phase 2: Enhance Automation Across the Plant with KoPilot

Challenge

Building on the success of Phase 1, Ayo Biomass aimed to further optimize key areas of plant operations, including production throughput, energy consumption, and resource utilization. The existing systems required excessive manual intervention and lacked the flexibility needed to optimize overall performance. The challenge was to implement a solution that enhanced operational efficiency while making the system transparent and manageable for the operations team.

Solution

Koidra introduced KoPilot, a decision-support platform designed to optimize production and energy use. KoPilot was implemented to enhance operational efficiency by integrating real-time data insights from DataPilot with advanced control strategies.

• The drying problem: Wood reaches the dryers at around 45 to 50% moisture and has to leave below 15%. The incoming moisture changes with the supplier, the species and the weather, so fixed settings either overdry, which wastes fuel, or underdry, which puts the pellets out of specification.

• What KoPilot controlled: The dryer setpoints and the combustion air ratio, moved against the moisture actually arriving rather than the moisture the last shift expected. Every move stayed inside limits the plant’s engineers set, and the existing control system carried it out.

• Production Optimization: KoPilot analyzed real-time data and adjusted the dryers continuously, aiming for higher throughput and more consistent output. While it ran, dryer operational efficiency rose by up to 20%.

• Energy Optimization: A major goal of implementing KoPilot was to reduce energy consumption during the drying and pelleting processes. The platform provided insights into energy use patterns, identifying areas where fuel and electricity could be used more efficiently.

• Operational Transparency: KoPilot simplifies complex plant processes by translating what was previously embedded in sophisticated PLC logic into understandable formulas and rules. This gives operations managers clear visibility into how decisions are made, empowering them to monitor and adjust processes as needed without relying on specialized programming knowledge.

By focusing on production and energy optimization, KoPilot empowered Ayo Biomass to reduce operational costs, minimize resource waste, and improve plant performance overall. This approach allowed the operations team to shift their focus from routine manual interventions to continuous process improvement.

What the dryers taught us

The gain did not hold in every period. The controller was not the weak point. A closed loop acts on what its instruments report, and at Ayo some of them were wrong: a temperature channel that froze at a safe-looking value, a vibration channel that read half the true figure. Some readings, such as the dryer exhaust temperature, had no agreed limit at all. The cracked dryer fan investigation shows both.

A loop that writes setpoints needs trusted instruments and limits the plant has signed off. Advice needs neither on day one, because a person checks it before anything changes.

Phase 3: Advice first, then the loop again

The first gain came before the foundation, which is why it did not hold. In 2026 Ayo is building that foundation in order. First, an asset model and maintenance on Koidra CMMS at both plants, so every reading and every repair belongs to a named piece of equipment. Then Predix: each week the plant gets an operating report, the actions that matter most ranked, each with the evidence behind it and the team that owns it. Closed-loop control returns to the dryers on that footing, with healthy instruments and agreed limits, so KoPilot runs on data the plant can trust.

Customer Testimonials

“Koidra’s DataPilot and KoPilot have made a big difference in how we run things. The real-time data and automation tools give us everything we need to keep improving our processes and production, all in one place. It’s helped us make quicker, smarter decisions and stay on top of things more easily.”, Trang Do, Continuous Improvement Manager, Ayo Biomass