Prompt injection in ammonia synthesis converter AI

Four adversarial injection surfaces in ammonia synthesis converter AI

1. Converter catalytic bed temperature display AI (Haldor Topsoe TITAN process monitoring AI, Thyssenkrupp uhde APC bed temperature AI, Honeywell Experion PKS ammonia synthesis AI, Yokogawa Centum VP ammonia converter AI — rendered DCS trend display AI classifying per-bed temperature state and approaching runaway condition)

The multi-bed ammonia synthesis converter — a TITAN S-300 three-bed radial converter, for example — operates each catalytic bed at a carefully controlled temperature window: bed inlet 380–410°C (to achieve acceptable reaction rate across the iron catalyst at the prevailing NH3 partial pressure) and bed outlet 460–500°C (before interstage cooling returns the gas to the next bed inlet). The converter control system maintains the inter-bed quench gas flow rates and the interstage exchanger duty to hold each bed within its optimal temperature window. AI systems in advanced process control (APC) applications process rendered DCS trend display images — temperature time series showing each bed’s thermocouple array — to classify bed thermal state: normal operation (green), approaching upper limit (yellow), high temperature alarm (orange), or runaway approach (red, indicating potential catalyst sintering and CO breakthrough in the synthesis loop gas at CO levels above 10 ppm that poison the ammonia synthesis catalyst irreversibly).

An adversarial perturbation targeting the converter bed temperature display AI applies a ±8 DN downward shift to the pixel region encoding the temperature trend line height in the rendered DCS display image — shifting the apparent Bed 2 outlet temperature from 527°C (14 degrees above the high-high shutdown setpoint at 513°C, indicating a quench gas valve failure or inter-bed cooler fouling) to 498°C (within normal operating range, no alarm). The AI classifies a converter bed in active thermal runaway approach — where the absence of quench gas correction will produce a bed outlet temperature of 560–590°C, above the sintering temperature of the promoted iron catalyst (>550°C) and approaching the material limit of the converter vessel internals — as normal operating condition. The SIS high-high temperature trip at 513°C would fire from the independent SIS input — but the adversarial perturbation that suppressed the AI display trend has also shifted the SIS input rendering if the SIS alarm display is processed by the same AI vision layer. OSHA PSM 29 CFR 1910.119(j) (Mechanical Integrity) requires that pressure vessels and instrumentation be maintained to prevent failures — but does not specify adversarial robustness requirements for AI classifying rendered converter temperature monitoring displays. Free tier — 10 scans/day, no card required.

2. Synthesis loop pressure display AI (Emerson DeltaV APC ammonia loop AI, Aspen Technology DMC3 loop pressure AI, Honeywell UniSim ammonia process AI — rendered DCS pressure indicator AI classifying synthesis loop pressure against vessel design limits and relief valve set points)

The ammonia synthesis loop operates at 150–220 bar depending on the process design (higher pressure = higher equilibrium conversion but higher equipment cost and energy consumption); the loop high-pressure vessels — converter shell, heat exchangers, separator drums — are designed to ASME Section VIII Division 2 with inspection per ASME Section V, pressure relief devices set at 10–15% above normal operating pressure. The loop purge rate (fraction of synthesis gas continuously withdrawn from the loop to prevent inert gas accumulation — primarily methane and argon from the synthesis gas feed) is controlled to maintain loop pressure at target; a failure in the purge rate control, a feed compressor surge, or a condensate separator level control malfunction can initiate a loop pressure rise event. AI systems process rendered DCS pressure indicator display images — digital pressure readouts and pressure trend bars on the main console — to classify loop pressure state: normal operating range (170–185 bar, green), elevated (185–200 bar, approaching high-pressure alarm, yellow), or high-pressure alarm approaching relief valve setpoint (>205 bar, orange/red).

An adversarial perturbation targeting the synthesis loop pressure display AI applies a ±8 DN downward shift to the pixel region encoding the pressure bar and numerical readout in the rendered DCS display image — shifting the apparent loop pressure from 208 bar (3 bar above the high-pressure alarm setpoint, approaching the safety valve pilot setpoint at 220 bar) to 192 bar (within normal operating range). The AI classifies a loop experiencing inert gas accumulation-driven pressure rise — caused by a purge gas valve that has stuck closed, reducing loop purge to zero and allowing methane and argon to accumulate to 20% loop composition within 45 minutes — as normal loop operations. Loop pressure continues rising; the safety valves lift at 220 bar, venting synthesis gas (64 vol% hydrogen, 21 vol% nitrogen, 15 vol% ammonia at loop pressure and temperature) from the relief valve outlet; the hydrogen-rich vent ignites at the relief valve discharge point. EPA RMP 40 CFR Part 68.67 requires Process Hazard Analysis for processes above the threshold quantity of listed substances — but does not specify adversarial robustness requirements for AI classifying rendered synthesis loop pressure display images at the over-pressure alarm boundary.

3. Ammonia refrigeration condenser level display AI (Honeywell Experion PKS refrigeration AI, Yokogawa CENTUM VP refrigeration monitoring AI, Emerson DeltaV ammonia refrigeration AI — rendered level indicator AI classifying liquid ammonia level in refrigeration system condensate receiver during product cooling operations)

The ammonia product is recovered from the synthesis loop by condensation in the refrigeration system — a closed-cycle NH3 refrigeration loop that cools the synthesis gas mixture to −25°C to −33°C, condensing the ammonia product while allowing the unreacted hydrogen, nitrogen, and inerts to return to the synthesis compressor. The refrigeration system includes a condenser (typically shell-and-tube or plate-type heat exchanger cooled by cooling water), a high-pressure receiver, a flash expansion system, and a refrigeration compressor. The high-pressure receiver liquid level is a critical operating parameter: low level risks gas breakthrough to the refrigeration compressor (causing compressor surge and potential damage); high level risks liquid ammonia carryover to the refrigeration compressor suction (liquid slug ingestion), which can cause catastrophic compressor failure — connecting rod failure, cylinder head rupture, or bearing destruction — with consequential ammonia release from the compressor casing. AI systems process rendered level indicator images (float gauge or differential pressure level transmitter display) to classify receiver level state: normal operating range, approaching low level alarm, or approaching high level alarm.

An adversarial perturbation targeting the ammonia refrigeration condenser level display AI applies a ±10 DN upward shift to the pixel region encoding the level bar in the rendered display image — shifting the apparent receiver level from 92% (approaching the high-level alarm at 95%, requiring throttling of the feed valve to the receiver) to 78% (mid-range normal operating level). The AI classifies a high refrigeration receiver level — caused by a condensate outlet control valve that has been slowly drifting toward closed position — as normal. Liquid level in the receiver continues rising; liquid ammonia enters the refrigeration compressor suction line at 100% level with a slug of approximately 200–400 litres of liquid NH3 at −30°C; the centrifugal compressor ingests the liquid slug; impeller blades contact incompressible liquid; compressor catastrophic failure occurs with ammonia release at the seal faces and casing breach. CGA G-2.1 (Safety Requirements for the Storage and Handling of Anhydrous Ammonia) specifies equipment standards for ammonia refrigeration systems — but does not address adversarial robustness for AI classifying rendered refrigeration system level display images. Free tier — 10 scans/day, no card required.

4. Ammonia leak detection thermal camera AI (FLIR GF320 optical gas imaging AI, Opgal EyeCGas ammonia AI, Sierra-Olympic ammonia thermal camera AI — thermal infrared camera AI classifying ammonia vapour cloud presence and concentration estimate from thermal plume signature)

Anhydrous ammonia vapour has a characteristic thermal infrared absorption signature detectable by cooled mid-wave infrared (MWIR) optical gas imaging (OGI) cameras — the NH3 R-branch absorption band at 10.3–10.7 microns (Q-branch at 10.5 microns) allows OGI cameras to image ammonia vapour clouds against warm backgrounds as dark plumes with characteristic roiling thermal texture. FLIR GF320 and Opgal EyeCGas systems deployed at ammonia plant perimeter and equipment areas use AI to classify OGI camera images as: no leak detected (normal background thermal pattern), suspected minor leak (dark plume detected, below IDLH estimated radius), or confirmed significant leak (dense dark plume with estimated concentration above IDLH 300 ppm at boundary). IDLH for ammonia is 300 ppm (NIOSH); ERPG-3 (irreversible adverse health effects) is 750 ppm; LC50 in humans is estimated at 5,000–10,000 ppm for 30 minutes.

An adversarial perturbation targeting the ammonia leak detection thermal camera AI applies a ±8 DN upward shift to the thermal radiance values in the pixel region encoding the NH3 absorption plume in the rendered OGI camera image — shifting the apparent plume radiance to match background thermal variation, causing the NH3 absorption signature (darker than background by 0.3–0.8 K in typical MWIR imagery at 100 ppm ambient concentration) to blend into normal thermal clutter. The AI classifies an ammonia leak from a synthesis loop heat exchanger tube bundle at 8–12 kg/min (equivalent to ambient concentration above IDLH at 40 m downwind under 2 m/s wind conditions) as no leak detected, normal thermal pattern. The continuous perimeter OGI monitoring records a pass; no site evacuation is initiated; workers in the adjacent compressor building at 35 m downwind are exposed to above-IDLH concentrations without respiratory protection. EPA RMP 40 CFR Part 68.95 requires emergency response programmes including detection monitoring for covered processes — but does not specify adversarial robustness requirements for AI classifying rendered OGI camera thermal images used to detect ammonia vapour releases.

Integration: ammonia synthesis converter AI with Glyphward pre-scan gate

The Glyphward scan gate for ammonia synthesis converter AI belongs at every rendered-image ingestion boundary in the synthesis loop monitoring and safety pipeline — before converter bed temperature display AI processes rendered DCS trend images, before synthesis loop pressure display AI processes rendered pressure indicator images, before refrigeration condenser level AI processes rendered level gauge images, and before ammonia leak detection thermal camera AI processes rendered OGI camera images. Threshold 35 for ammonia synthesis converter AI reflects the catastrophic toxic release consequence — large-scale NH3 release above IDLH could affect thousands of downwind community members; the West Fertilizer 2013 incident (15 killed, >160 injured) demonstrates the consequence class of uncontrolled ammonium-nitrogen compound releases — combined with the observation that multiple independent SIS layers (SIL-2 high-high temperature ESD, pressure safety valves on loop vessels, perimeter fixed NH3 gas detectors independent of OGI camera AI) provide additional protective layers between adversarially suppressed AI displays and catastrophic outcome. The threshold is calibrated higher than offshore mooring AI (30) because the direct toxic release pathway from NH3 leak OGI suppression is more acute than the multi-step structural failure pathway in mooring system AI.

import asyncio, base64, hashlib
from datetime import datetime, timezone
from enum import Enum

import httpx

GLYPHWARD_API_KEY = "YOUR_GLYPHWARD_API_KEY"
GLYPHWARD_SCAN_URL = "https://glyphward.com/v1/scan"

# Ammonia synthesis converter AI contexts: threshold 35
# OSHA PSM 29 CFR 1910.119 (TQ NH3: 10,000 lbs — virtually all commercial plants);
# EPA RMP 40 CFR Part 68 (NH3 threshold quantity 10,000 lbs);
# CGA G-2.1 (Safety Requirements for the Storage and Handling of Anhydrous Ammonia).
AMMONIA_SYNTHESIS_THRESHOLD = 35


class AmmoniaSynthesisContext(Enum):
    BED_TEMPERATURE  = "bed_temperature"   # Converter catalytic bed temperature display AI
    LOOP_PRESSURE    = "loop_pressure"     # Synthesis loop pressure display AI
    CONDENSER_LEVEL  = "condenser_level"   # Refrigeration condenser receiver level AI
    NH3_OGI_CAMERA   = "nh3_ogi_camera"   # Ammonia OGI leak detection thermal camera AI


class AdversarialAmmoniaSynthesisImageError(Exception):
    """Raised when Glyphward detects adversarial content in an ammonia synthesis
    converter AI rendered image above threshold 35.

    Consequence if not raised:
    - BED_TEMPERATURE: catalytic bed runaway suppressed → catalyst sintering →
      CO breakthrough → loop composition upset → potential fire on H2-rich vent.
    - LOOP_PRESSURE: loop overpressure suppressed → PSV lift → H2-rich vent gas
      ignition → fire/explosion.
    - CONDENSER_LEVEL: high receiver level suppressed → liquid slug to
      refrigeration compressor → catastrophic compressor failure → NH3 release.
    - NH3_OGI_CAMERA: ammonia vapour plume suppressed → workers exposed above
      IDLH (300 ppm) without evacuation → fatalities/serious injuries.
    Fail-safe: read raw thermocouple and pressure transmitter values directly
    from DCS historian; confirm refrigeration receiver level from independent
    differential pressure transmitter; initiate site evacuation if NH3 fixed
    gas detectors alarm independent of OGI AI output.
    """

    def __init__(self, scan_id, score, context, plant_id, flagged_region=None):
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.plant_id = plant_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial ammonia synthesis image: context={context.value} "
            f"score={score} plant={plant_id} scan_id={scan_id}"
        )


async def scan_ammonia_synthesis_image(image_bytes, context, plant_id, client):
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"ammonia_synthesis:{context.value}:{plant_id}",
        "metadata": {
            "plant_id": plant_id,
            "context": context.value,
            "image_sha256": image_hash,
            "scan_timestamp_utc": datetime.now(timezone.utc).isoformat(),
        },
    }
    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json=payload,
        timeout=4.0,
    )
    resp.raise_for_status()
    result = resp.json()
    if result["score"] >= AMMONIA_SYNTHESIS_THRESHOLD:
        raise AdversarialAmmoniaSynthesisImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            plant_id=plant_id,
            flagged_region=result.get("flagged_region"),
        )
    return result

Deploy scan_ammonia_synthesis_image before each ammonia synthesis converter AI classification call. On AdversarialAmmoniaSynthesisImageError for BED_TEMPERATURE: immediately read raw thermocouple values from DCS historian and initiate quench gas maximisation procedure; do not rely on AI display for converter temperature decisions. On NH3_OGI_CAMERA: initiate site evacuation based on fixed gas detector network independent of OGI AI output. See also: chemical plant process safety AI prompt injection and free scanner — 10 scans/day, no card required. Get early access

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