End-to-End Process Parameter Optimization
Unified data management for precise process control and energy optimization across the full production line — reducing cost, improving quality, and cutting carbon emissions.
Request DemoSteel production involves long process chains, complex operating conditions, and tightly coupled workflows — where precise control of process parameters directly determines product quality, energy consumption, and operational efficiency.


Built-in OPC UA, ODBC, and other protocol plugins enable unified connectivity across production equipment and business systems — no custom development required, with centralized interface management and multi-source data consolidation.
A real-time monitoring pipeline for roasting and other core processes ingests process parameters into stream tables, computes rolling means, variance, and rate of change via window calculation, and triggers instant alerts when values exceed preset thresholds.
Rich built-in functions and vectorized operations support model building and optimization solving in one place. Deep integration of stream processing and machine learning enables real-time inference with millisecond response and strong generalization across process conditions.
Designed for high-concurrency ingestion and analysis of massive time-series datasets, DolphinDB supports real-time process control and intelligent optimization at production scale — with a flexible architecture that integrates into existing industrial environments without vendor lock-in.
Low-latency stream processing and model integration enable precise parameter control across the full production line — reducing process deviations, minimizing equipment downtime, and stabilizing thermal conditions to improve product consistency.
By uncovering relationships between process parameters and energy consumption, DolphinDB drives optimization of equipment and production line strategies — significantly reducing overall energy use while maintaining output and quality targets.
Physics-informed and data-driven modeling built on DolphinDB's native compute capabilities produces generalized models that can be reused across production lines and adapted to dynamic process changes — accelerating the scaling of digital initiatives.