Kausalyze builds a causal model of one process unit from 12 to 24 months of historical data, constrains it with process physics and validates the important relationships with your engineers. Use it to trace failures to upstream drivers, test interventions and prescribe changes. First validated root causes in 12 to 16 weeks.
Historian time-series data, P&IDs or PFDs, lab or quality data and engineering knowledge provide the inputs. Kausalyze proposes time-dependent causal relationships, applies physical constraints and presents the important relationships for engineering review.
12 to 24 months of time series data.
P&IDs or PFDs to represent the topology.
Process knowledge and a few hours a week during review to inspect, confirm or reject important proposed relationships.
Quality samples and assay results, so off-spec product is tied back to the conditions that produced it.
Causes may take minutes, hours or days to appear downstream. The model accounts for these delays when linking a driver to its effect.
The model distinguishes variables that drive an outcome from variables that simply move alongside it, using time-series evidence, process constraints and engineering knowledge to pin point true drivers.
Mass and energy balances and process connectivity constrain the model to prevent physically impossible solutions.
Engineers can review, change, or challenge the model directly, and update it to reflect the plant operation, enabling preservation of institutional knowledge.
Every conclusion can show the causal path behind it, from upstream driver to downstream outcome, with evidence engineers can inspect.
Identify upstream drivers of recurring trips and assess interventions that could reduce unplanned stops.
Compare operating windows and set-point changes for their expected effect on quality, production rate and downstream limits.
Find the conditions driving excess steam, power use or repeat maintenance, and estimate the value of changing them.
One causal model supports five operational workflows. Inspect the evidence, review important relationships with engineers and compare the expected impact before acting.
Trace a recurring trip, quality problem or process instability back to the variables driving it. Review the ranked upstream drivers, causal path and evidence for each relationship.
Estimate when a condition is likely to cross a threshold. Review the operating state, forecast confidence and evidence behind the estimate to plan an intervention.
Change a set-point in the model and inspect the predicted downstream effect. Compare it with leaving conditions unchanged, including the expected effect on process limits.
Compare the set-point change, maintenance intervention or operating window most likely to improve the outcome. Each recommendation shows its expected impact and the downstream variables it may affect.
Use the causal path to connect a proposed change to a measurable plant outcome. Translate expected impact into downtime avoided, production recovered or energy saved. Decisions that translate into real-world savings.
Control systems operate the plant. Monitoring systems collect data and flag conditions. Kausalyze provides the causal reasoning layer between those observations and operational decisions.
From plant data to operational decisions
Failures rarely respect equipment boundaries. Kausalyze models relationships across connected equipment, process units and shared utilities. The pilot begins with one scoped unit and its relevant upstream drivers.
Every finding comes with the causal path and supporting evidence. Engineers can inspect, confirm or reject the important relationships before acting on them.
Process structure, engineering knowledge and physical constraints are part of the model rather than an afterthought. Mass and energy balances and process connectivity constrain the proposed causal relationships. Built by process engineers for process engineers.
Continuous and batch process industries, wherever connected equipment and shared utilities make cause hard to separate from effect.
Choose one unit and one recurring, costly problem. Agree the target outcome and arrange a secure handoff of historical historian data, tag definitions, engineering units, process drawings and relevant lab or quality data.
Use 12 to 24 months of historical data, process drawings and relevant process information to propose time-dependent causal relationships. Apply mass and energy balances and process connectivity as constraints, accounting for the operating conditions represented in the data.
We review important causal relationships and root-cause findings with your engineers. Inspect the variables, direction, time lag, strength and supporting evidence. Engineers can confirm or reject proposed relationships; the model is updated to reflect the review.
Identify set-point changes, maintenance interventions or operating windows and test their expected downstream impact in the model. Quantify the value using agreed plant measures, production and cost data. The readout supports a decision about whether and where to expand next.
Predictive maintenance usually focuses on asset condition and the likelihood of failure. Kausalyze models causal relationships across connected equipment and utilities to identify upstream drivers and assess what to change. The pilot starts with one unit and one recurring problem.
General-purpose language models are useful interfaces for industrial information, but they do not provide the process-specific causal model needed to reason about physical cause and effect. Probablistic predictions are prone to hallucinations, making them impossible to use in decision-critical applications. Kausalyze builds that model from plant data, constrains it with process physics and validates important relationships with engineers.
For a pilot, time-series export from your historian for the unit, with the tag list and engineering units. CSV is fine. We confirm the format and sample interval on the call.
No. The pilot can be built offline from historical historian data, process drawings and relevant lab data. A live-system connection is not required to build and validate the first model.
Kausalyze engineers build and validate the model. Your process engineer reviews the findings in scheduled sessions and answers questions about the unit. Expect a few hours a week during the review phase.
We assess gaps, tag changes and periods of bad data at the start. Missing or unreliable measurements may limit which relationships and findings can be supported. The data quality review identifies those limits and any additional information needed.
A causal map with supporting evidence, ranked root causes validated with your engineers, prescribed changes with expected impact, and a value assessment based on agreed site measures. The readout includes remaining uncertainties and a recommendation on whether and where to expand.
Each pilot is scoped and quoted per unit. Ask on the call.
The one with a recurring problem nobody has fully explained, a well-populated historian, and a known cost per trip. We help you pick it on the call.
Use the findings and value assessment to decide whether to expand to adjacent units or additional problems. Ongoing data updates and any live-data integration are scoped separately from the pilot.
Bring one recurring plant problem. We will review the unit, available data and the outcome you want to improve, then define a practical pilot scope.
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