Digital, AI & Smart Engineering Solutions

Kerone develops supervised learning models for quality prediction, reinforcement learning for process optimization, deep learning for computer vision inspection, and physics-informed neural networks for process simulation.

Yes, Kerone offers on-premise and edge deployment options for AI systems, ensuring that sensitive production data remains within the client's own infrastructure.

MPC is an advanced control strategy that uses a process model to predict future system behavior and compute optimal control actions. Kerone deploys MPC to optimize multi-variable industrial processes in real time.

Kerone builds digital twins using a combination of physics-based models, historical process data, and real-time sensor feeds. The twin is continuously updated to maintain accuracy as the physical system evolves.

Yes, Kerone's AI and digital solutions integrate with existing SCADA, DCS, MES, and ERP platforms via standard APIs, OPC-UA, and database connectors.

A typical AI optimization system deployment takes 8 to 20 weeks, depending on data availability, process complexity, and integration requirements.

Kerone's solutions can work with existing SCADA or historian data. Kerone also assists clients in building the necessary data infrastructure including sensors, edge nodes, and time-series databases.

CFD simulation can replace many physical tests during the design phase, reducing time and cost. However, for critical applications, Kerone recommends validating CFD results with targeted physical trials.

Industrial processes drift over time due to equipment wear, raw material variation, and seasonal effects, which means AI models trained on historical data gradually lose accuracy if left static. Kerone builds ongoing model monitoring into deployed systems, tracking prediction accuracy against actual outcomes and flagging when performance degrades beyond an acceptable threshold. Periodic retraining using recent operating data keeps models aligned with current process conditions, and physics-informed model architectures tend to degrade more gracefully than purely data-driven models when conditions shift outside the original training range. Establishing a clear ownership structure for ongoing model maintenance, whether handled internally by the client's data team or through an ongoing service agreement with Kerone, is essential, since a deployed AI system without an ongoing maintenance plan typically becomes unreliable within months as the underlying process evolves.

Traditional PID and rule-based control excel at maintaining stable setpoints for well-understood, single-variable relationships, but struggle with complex, multi-variable processes where the optimal control action depends on interactions between many parameters simultaneously. AI-based approaches like Model Predictive Control can account for these interactions and anticipate future process behavior rather than only reacting to current deviation, often achieving tighter control and better energy or yield optimization in genuinely multi-variable processes. However, traditional control remains entirely appropriate and more cost-effective for simpler control loops where multi-variable optimization offers little additional benefit. The decision isn't AI versus traditional control as a blanket choice, but rather identifying which specific control loops in a plant have enough complexity and interaction to benefit from advanced techniques, while leaving simpler loops on proven traditional control.

Cloud-based AI deployments offer easier scalability and centralized model management but require transmitting potentially sensitive production data outside the client's own infrastructure, which raises concerns for companies with strict data governance policies or competitively sensitive process information. Edge deployments keep all data processing within the client's own network, eliminating this exposure but requiring more substantial local computing infrastructure and more hands-on maintenance by the client's own IT team. Hybrid architectures, where sensitive raw data stays on-premise while only aggregated or anonymized insights move to the cloud, offer a middle path that many manufacturers find acceptable. Kerone supports all three architectures and works with the client's IT and security teams to determine which approach satisfies their specific data governance requirements before finalizing the deployment architecture for an AI project.

Yes, once a model architecture and data pipeline are validated for one process or plant, the underlying framework typically transfers to similar processes at other locations with retraining on the new site's specific data rather than building an entirely new system from scratch. However, each additional plant or product line still requires sufficient historical or newly collected data specific to its own equipment and operating conditions, since models trained on one facility's data don't automatically generalize perfectly to another facility with different equipment vintage, raw materials, or operating practices. Standardizing data collection and tagging conventions across multiple plants in advance significantly accelerates this scaling process. Companies planning a multi-site AI rollout benefit from treating the first deployment partly as a template-building exercise, documenting what worked, what data was needed, and what customization was required for that specific process.

Processes with significant multi-variable complexity and interacting parameters, such as drying and thermal processing where temperature, humidity, airflow, and material properties all interact, benefit substantially from AI-based optimization that traditional control struggles to address. Quality-critical processes in pharmaceutical and food manufacturing benefit from AI-based defect detection and batch release prediction that reduces both quality risk and unnecessary destructive testing. Energy-intensive processes such as furnaces, kilns, and large drying systems benefit from AI-driven energy optimization given the direct cost impact of even modest efficiency gains at scale. Processes with rich existing historian data and a track record of operational variability that hasn't yet been systematically addressed represent the best near-term opportunities, since both the data foundation and the improvement potential already exist, reducing the time needed to demonstrate measurable value.