HAZOP — Deviations From Design Intent

Hazard and Operability study asks, guided word by guided word, what happens if a process parameter is too much, too little, reversed, or accompanied by something unintended — a process/engineering tool applied here to a chromatography example.

The one idea

HAZOP doesn’t start from a list of known failure modes the way FMEA does — it starts from the process’s own design intent and systematically asks what happens if reality deviates from it, one guide word at a time, parameter by parameter.

Mechanics

For each parameter at each step of a process (flow rate, temperature, pressure, pH, concentration, time), a HAZOP team applies a fixed set of guide words and asks what a deviation of that kind would actually cause:

Guide word	Meaning	Generic example
NO	The parameter is completely absent	No flow — pump failure
MORE	The parameter is higher than intended	More pressure than the system is rated for
LESS	The parameter is lower than intended	Less temperature than the reaction requires
AS WELL AS	Something additional is present	An unexpected contaminant enters with the intended feed
REVERSE	The parameter or flow runs backward	Reverse flow through a check valve that has failed
OTHER THAN	Something completely different happens instead	A different reagent is charged than intended

Unlike FMEA, HAZOP doesn’t score every deviation on Severity/Occurrence/Detection — the output is a qualitative list of credible deviations, their causes, consequences, and existing safeguards, with follow-up actions where the safeguards look thin.

Worked example — HPLC flow rate and a bioreactor’s temperature

Guide word	Parameter	Deviation	Consequence	Safeguard
MORE	HPLC flow rate	Pump set point drifts high	Column overpressure, potential seal failure, resolution loss	System pressure alarm, method-defined pressure limit
LESS	HPLC flow rate	Partial pump blockage	Retention times shift, poor resolution between API and impurity	System suitability retention-time check
NO	HPLC flow rate	Pump stalls	No separation occurs at all; run aborts	Run-sequence software flags a failed injection
MORE	Bioreactor temperature	Heating control fails open	Reduced cell viability, altered glycosylation profile (a CQA hit)	Independent high-temperature interlock, separate from the control loop
LESS	Bioreactor temperature	Cooling jacket over-corrects	Reduced growth rate, extended run time	Continuous temperature logging with trend alarms

Notice the bioreactor row: a MORE temperature deviation doesn’t just risk an obvious failure (dead cells) — it can silently shift a critical quality attribute (glycosylation) while the culture still looks healthy, which is exactly the kind of consequence a guide-word walk-through is designed to surface deliberately, rather than relying on someone to have already thought of it.

When to reach for it vs. FMEA

HAZOP and FMEA overlap heavily in outcome — both end up identifying deviations and their consequences — but HAZOP is organized around the process’s design intent, parameter by parameter, which makes it a natural fit for engineering and process-design teams examining a new unit operation (a reactor, a filtration skid, a chromatography skid) before it’s ever run. Most QC labs default to FMEA for method risk because the “steps” of a method are already well defined; HAZOP earns its keep more in process/engineering contexts where the parameters, not discrete process steps, are the natural unit of analysis.

Known weaknesses

Applying every guide word to every parameter at every step can be slow and exhaustive for a complex process — teams often scope it to the parameters most likely to matter, which reintroduces some of the same judgment calls HAZOP is meant to avoid.
Without a scoring step, prioritizing which deviations to act on first is a separate, later exercise — HAZOP tells you what could deviate, not which deviation matters most.
The overlap with FMEA means running both on the same process is often redundant; most sites pick one as the primary tool for a given risk type (HAZOP for process design, FMEA for methods) rather than running both routinely.