Classical TRIZ is powerful — but it was built for a world of gears, levers, and mechanical assemblies.
That matters.
In mechanical systems, contradictions are often physical and visible:
• Stronger material → heavier part
• Faster motion → more wear
• Higher force → more deformation
The parameters are tangible.
The mechanisms are clear.
Process industries are different.
In chemical, biochemical, food, and pharmaceutical engineering, contradictions hide inside states, rates, and couplings that don’t appear on a drawing.
Consider how problems are typically framed:
• “We need better quality”
• “We need higher productivity”
• “We need lower cost”
Classical TRIZ maps these to abstract parameters like Quality, Productivity, or Cost.
That’s where things break.
In process systems:
• “Quality” could mean selectivity, purity, crystal morphology, polymorph stability, or biological activity
• “Cost” could mean yield loss, solvent recovery, cycle time, scrap rate, or rework under GMP constraints
• “Productivity” could mean space-time yield, conversion per pass, batch turnaround, or equipment utilization
Each is governed by different physics — and different regulatory risks.
When we collapse them into outcome-level labels, three problems appear.
1. Ambiguity
Two engineers map the same problem to different TRIZ parameters — both “correct,” neither useful.
2. Lost mechanisms
Increasing temperature might improve conversion, accelerate degradation, shift equilibrium, reduce viscosity, and shrink safety margins at the same time.
Treating “cost” or “quality” as a single knob erases the real constraint.
3. False solution confidence
The contradiction matrix suggests principles — but not why they apply, or when they silently fail in continuous or regulated operation.
This is why TRIZ sessions in process industries often stall.
I’ve seen this repeatedly in real scale-up, optimization, and remediation work.
• The framing feels forced
• The principles feel generic
• The leap from abstraction to implementation feels unsafe
So teams revert to optimization.
Or experience.
Or trial-and-error.
None of this means TRIZ is wrong.
It means classical TRIZ is under-specified for systems where contradictions live at the level of:
• thermodynamic state
• transport limitation
• phase behavior
• geometry–rate coupling
• interfacial and surface phenomena
Until those are explicit, TRIZ looks theoretical — even when it’s pointing in the right direction.
This is also why AI struggles here.
If parameters are vague, AI learns correlations without causality.
If mechanisms are hidden, AI amplifies confidence instead of understanding.
Next: why many “AI + TRIZ” approaches accidentally optimize abstractions — and how that failure mode hides in plain sight.
If you’ve ever felt TRIZ was interesting but impractical for real plants or regulated products — this is likely why. What gets labeled ‘quality’ or ‘cost’ in your work — but actually hides a contradiction you’ve never been able to optimize away?