Honeyshell Tech provides an AI‑enhanced advanced process control platform for energy‑intensive manufacturers such as cement plants and power‑generation facilities.
Funding
Funding not disclosed
Founders
Product
Problem
Energy‑intensive manufacturers such as cement plants and power‑generation facilities rely on complex, tightly coupled processes that are difficult to monitor and adjust in real time. Inefficient control leads to higher fuel consumption, reduced throughput, and increased safety risks.
Solution
Honeyshell Tech delivers an AI‑enhanced advanced process control platform that ingests sensor data, builds predictive models, and automatically adjusts operating parameters to maintain optimal performance. The system provides continuous, real‑time monitoring dashboards and alerts, enabling operators to intervene only when necessary. By integrating machine‑learning optimization with traditional control loops, the platform improves energy efficiency, boosts productivity, and enhances safety without requiring extensive manual tuning. The solution is packaged as a modular software suite that can be deployed on existing plant infrastructure and customized to specific process configurations.
Target Audience
Primary customers are process engineers and operations managers at cement manufacturers, power plants, and other energy‑intensive industrial facilities seeking to improve efficiency and safety.
Features
- Data acquisition layer that aggregates high‑frequency sensor streams from PLCs, DCS, and IoT devices
- Physics‑informed machine‑learning models that predict key performance indicators such as fuel consumption, emissions, and equipment wear
- Closed‑loop automated control actions that adjust setpoints for kilns, mills, boilers, and related equipment
- Real‑time visualization dashboards with KPI trends, anomaly detection, and operator alerts
- Integration adapters for common industrial protocols (OPC-UA, Modbus, MQTT) and easy API connectivity to existing control systems
- Scenario simulation tools that allow engineers to evaluate process changes before implementation