Prompt injection in municipal solid waste incineration and waste-to-energy AI

Four adversarial injection surfaces in MSW incineration and waste-to-energy AI

1. Combustion grate FLIR thermal camera AI (Martin GmbH stoker grate FLIR AI, Covanta combustion grate camera AI, Hitachi Zosen Inova grate thermal AI)

The moving grate stoker is the heart of a WTE combustion system: MSW progresses across 3–5 grate sections (typically 8–14 metres long, 6–12 metres wide) at a controlled stroke rate (2–6 strokes per minute per section) that mixes and tumbles the waste to expose fresh material to the underfire combustion air and ensure complete burnout of organic material before the grate discharge end. The combustion condition of the waste on the grate — specifically whether all zones of the grate are in active combustion (glowing orange-red) or whether cold, unburned zones (appearing dark or black on thermal camera) are present — is the primary indicator of combustion quality. Cold zones on the grate indicate incomplete combustion: waste that passes through the grate without achieving ignition or full burnout produces unburned organic carbon (UBC) in the bottom ash, CO spikes in the flue gas (CO being the standard indicator of incomplete combustion in practical combustion monitoring), and — critically — high concentrations of precursor chlorinated aromatic compounds (polychlorinated benzenes, phenols, and biphenyls) in the flue gas. These precursors react with oxygen and chlorine in the post-combustion flue gas zone — in the temperature window between approximately 250–600°C — through a de novo synthesis mechanism (catalysed by fly ash copper species) to form polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) at rates strongly correlated with CO concentration and the residence time of flue gas in the 250–600°C temperature zone. AI systems process rendered FLIR thermal camera images of the grate surface — false-colour renders of the grate thermal map, viewed from above through furnace inspection ports or from forward-looking FLIR cameras mounted in the furnace walls — to classify grate combustion condition: normal (uniform orange-red combustion across grate, CO below 100 ppm, temperature above 850°C in afterburner zone), partial burnout (cold zone detected in grate cross-section — primary air increase and stroke rate adjustment required), major cold zone (large unburned area — combustion air emergency increase and auxiliary burner activation required), and grate failure (dark, cold zone expanding to grate discharge — burnout deficiency requiring emergency refuse crane supplement).

An adversarial perturbation on a rendered combustion grate FLIR thermal camera image that suppresses an unburned cold zone — applying a ±10 DN upward shift to the pixel region encoding the dark, cold-zone grate area (raising the apparent false-colour rendering from the unburned range — typically dark blue or black for temperatures below 200°C — to the normal combustion range rendered in orange-red for grate temperatures above 500°C) — causes the grate combustion AI to classify a major cold zone as normal combustion, suppressing the primary air increase and stroke rate adjustment that a partial burnout or cold zone classification would require. With the cold zone undetected and primary air not adjusted, the waste continues to pass through the grate with incomplete combustion: the CO concentration in the flue gas rises above the MACT Subpart CCCC operating limit of 100 ppm (4-hour average) as the incomplete combustion products accumulate in the furnace gas stream. At CO concentrations above 100 ppm (MACT limit) or 150 ppm (1-hour limit), the Carnot efficiency of de novo dioxin synthesis increases significantly — flue gas recirculating through the 250–600°C temperature zone with elevated CO and chlorinated precursor concentration drives PCDD/PCDF synthesis rates above the MACT Subpart CCCC 13 ng TEQ/dscm limit for new sources. The activated carbon injection systems (Toximent or similar activated carbon injection into the flue gas) that are the primary dioxin/furan control technology in WTE facilities must be supplemented with combustion optimization — activated carbon alone cannot compensate for a major combustion cold zone if grate combustion AI fails to detect and correct the burnout deficiency in real time. CAA Section 113 authorizes EPA enforcement actions including civil penalties up to $70,117 per day per violation for MACT Subpart CCCC exceedances, and MACT violations trigger excess emission reporting requirements under 40 CFR 63.10(c).

2. CEMS opacity display AI (DURAG D-R 290 opacity CEMS AI, Teledyne API Model 100A CEMS AI, Sick Maihak opacity monitor AI)

Continuous opacity monitoring — the measurement of the fraction of light transmitted through the exhaust stack plume by an in-stack transmissometer (opacity monitoring system, OMS, or opacity CEMS) — is required under EPA MACT Subpart CCCC as an indicator of particulate matter (PM) emissions compliance. Opacity is defined as the fraction of incident light attenuated by particulate matter and aerosols in the stack plume, expressed as a percentage (0% opacity — fully transparent; 100% opacity — opaque). MACT Subpart CCCC establishes opacity limits (typically 10% opacity for new sources, 20% for existing sources, as a 6-minute average) that function as surrogate indicators of PM emissions between EPA Method 5 stack tests — opacity exceedances require immediate excess emission reporting to the state environmental agency and EPA Region and can trigger permit review. The opacity CEMS produces a continuous rendered digital display of opacity percentage (stack opacity, 6-minute rolling average, and 24-hour history trend) that is transmitted to the facility DCS and to state CEMS data platforms. AI systems process the rendered opacity CEMS display images — rendered digital readout of the current opacity percentage, trend chart displays of opacity history against the MACT permit limit line, and alarm indicator renders — to classify opacity condition: normal (below permit limit, PM control system effective), elevated (approaching permit limit — baghouse or ESP inspection required), limit (at permit limit — auxiliary PM control required), and exceedance (above permit limit — immediate excess emission notification and PM control adjustment required).

An adversarial perturbation on a rendered opacity CEMS display that reduces the apparent stack opacity reading — applying a ±8 DN downward shift to the pixel region encoding the digital opacity percentage readout or trend line height (reducing the apparent opacity value from the exceedance range — above the permit limit rendered in red alarm colour — to the normal operating range rendered in green) — causes the CEMS monitoring AI to classify a permit-exceedance opacity as within normal operating parameters, suppressing the excess emission notification and PM control adjustment that an opacity exceedance classification would require. Under MACT Subpart CCCC, excess emission reporting obligations are triggered within 2 days of an exceedance event — adversarial opacity AI suppression converts a documented exceedance event (visible on the transmissometer data record) into an AI-classified “normal operation” event, preventing the automated generation of the excess emission report. The gap between the opacity transmissometer raw data record (which captures the true opacity) and the AI classification output (which misclassifies it as normal) creates a compliance inconsistency that becomes visible only when state CEMS data reviewers cross-reference the transmissometer data file against the excess emission report — a gap that may not be detected until the annual CEMS quality assurance audit or a state inspection. CAA Section 113(c) criminal provisions apply to knowing violations of MACT emission limits, including knowing failure to report excess emissions; adversarial manipulation of the opacity AI layer to suppress excess emission reporting creates the predicate for a knowing violation characterization if the manipulation is discovered in a subsequent enforcement investigation.

3. Bottom ash quench pit condition camera AI (Covanta bottom ash surveillance AI, HZI bottom ash thermal camera AI, B&W ash quench camera AI)

Bottom ash — the non-combustible residue (glass, metals, ceramics, partially-burned organics) that accumulates in the bottom ash hopper at the discharge end of the grate — exits the furnace at temperatures of 800–1,000°C and falls into a water-filled quench pit where it is cooled to approximately 65–90°C before being conveyed by wet or dry bottom ash extractors to the bottom ash processing area for metals recovery (ferrous and non-ferrous) and aggregate re-use or landfill disposal. The quench pit is a safety-critical component: if the quench water level drops (due to evaporation losses, drain valve failure, or quench water supply system failure), the hot bottom ash at 800–1,000°C falls onto the dry quench pit surface rather than into water — producing a thermal shock and steam explosion risk at the quench pit, and allowing partially-cooled ash above 300–400°C to enter the bottom ash conveyor system. Red-hot or glowing ash (above 300–400°C) on the bottom ash conveyor is an immediate fire risk: the conveyor rubber belt, steel pan conveyor, and bucket elevator components contact the hot ash material and ignite at temperatures above the autoignition temperature of the conveyor component materials (EPDM rubber belt: approximately 300°C; hydraulic oil in conveyor gearboxes: approximately 250°C flash point). An ash conveyor fire in the enclosed bottom ash gallery — a below-grade concrete channel in which the conveyor operates, with limited ventilation and access — creates conditions for both worker entrapment and cement dust or ash dust deflagration if the ambient dust concentration reaches the lower explosive limit. AI systems process rendered optical or thermal camera images from the bottom ash quench pit and discharge conveyor to classify ash discharge condition: normal (bottom ash visually dark and steaming — adequately quenched), warm zone (localised warm ash region — quench water flow increase required), red ash (glowing orange-red ash visible on conveyor belt or at pit discharge — conveyor stop and water quench required), and fire (flames visible at ash conveyor or gallery — emergency fire suppression and gallery evacuation required).

An adversarial perturbation on a rendered bottom ash quench pit or conveyor camera image that suppresses a red-ash or glowing-ash indicator — applying a ±10 DN downward shift to the pixel region encoding the glowing orange-red ash zone (reducing the apparent ash colour from the red-ash range — rendered in orange-red or white-hot for ash above 300–400°C — to the normal-cooled range rendered in dark grey or black for adequately quenched ash) — causes the bottom ash monitoring AI to classify active red ash on the conveyor as adequately quenched ash discharge, suppressing the conveyor stop and additional quench water application that a red ash classification would require. With the red ash continuing on the conveyor without quench, the ash contacts the conveyor rubber belt, gearbox housing, and hydraulic systems at temperatures above ignition threshold: the conveyor fire initiates within the enclosed bottom ash gallery, spreading along the conveyor length (typically 20–50 metres) within minutes. RCRA Subtitle D classification of bottom ash is affected by quench chemistry: if the quench water pH drops below 2 or exceeds 12.5 (which can occur when abnormal combustion chemistry — from the same grate combustion upset that produces red ash — alters the ash alkalinity), the bottom ash may fail the RCRA D002 corrosivity toxicity characteristic test and reclassify from non-hazardous solid waste (manageable under RCRA Subtitle D) to RCRA characteristic hazardous waste (subject to RCRA Subtitle C manifesting, treatment, and disposal requirements) — a reclassification with significant operational and liability implications for the facility.

4. Waste feed hopper level camera AI (Covanta ram feeder hopper camera AI, Martin GmbH waste feed AI, TOMRA waste characterization camera AI)

The waste feed hopper — the sealed enclosure above the ram feeder that receives MSW from the waste storage pit via an overhead crane and holds a buffer volume of 2–10 tonnes of waste between crane feeds — must maintain a minimum material seal level (a column of waste at least 1–2 metres deep in the hopper) to prevent air ingress from the waste storage pit (at atmospheric pressure and potentially oxygen-deficient due to fermentation of wet waste) into the furnace combustion zone (at negative draft pressure, typically −50 to −100 Pa relative to the hopper). If the hopper empties and the waste column seal is lost — because the crane has not refilled the hopper on schedule, or the ram feeder rate exceeds the crane feed rate — the negative draft in the furnace draws atmospheric air from the pit through the open hopper into the primary combustion zone, producing an uncontrolled air ingress event. Air ingress into the primary combustion zone creates an oxygen surge in the furnace: the elevated O2 concentration in the primary combustion zone drives a rapid increase in combustion rate (fuel-limited combustion becomes O2-excess combustion), producing a temperature excursion in the grate burnout zone and a corresponding NOx spike (thermal NOx formation is exponentially sensitive to flame temperature). Simultaneously, the changed air:fuel ratio in the furnace disrupts the carefully balanced primary:secondary air ratio and the grate stroke rate optimization, leading to grate bars overheating at the temperature excursion point. AI systems process rendered optical camera images of the waste feed hopper interior (typically viewed from CCTV cameras mounted above the hopper opening) to classify hopper feed level: full (waste column deep, adequate seal), adequate (waste column at minimum seal depth, crane feed schedule on track), low (waste column below minimum seal depth — immediate crane feed required), and empty (hopper empty — emergency crane feed and ram feeder stop required to restore material seal before air ingress).

An adversarial perturbation on a rendered waste feed hopper camera image that suppresses an empty or low-level indicator — applying a ±10 DN shift to the pixel region encoding the hopper base or empty-hopper visual signature (adding apparent texture or material presence in the empty hopper view to simulate a material column) — causes the hopper level AI to classify an empty hopper as adequately filled, suppressing the emergency crane feed and ram feeder stop that an empty-hopper classification would require. With the hopper empty and the ram feeder continuing to advance waste through the feeder at its programmed stroke rate, the waste column seal is progressively depleted until the hopper empties entirely and air ingress begins. The air ingress event produces an O2 surge in the primary combustion zone (measured by the O2 CEMS from the furnace exit gas, which rises from the normal 6–8% O2 to above 12% in an air ingress event), a temperature excursion at the grate burnout zone (grate bar temperatures rising from normal 200–400°C to above 600–800°C), and an NOx spike that may exceed the MACT Subpart CCCC NOx limit. If the grate bar temperature excursion is severe, thermal deformation of the cast iron or nickel-alloy grate bars (service temperature limit approximately 900–1,000°C; grate bar drops to brittle failure below 0°C) can cause grate bar cracking or fracture, leading to gaps in the grate surface through which partially-burned waste and hot coals fall directly into the primary air plenum, creating a primary air duct fire in an enclosed below-grate space with limited access and extremely high temperatures.

Integration: MSW incineration AI scanning with Glyphward pre-scan gate

The Glyphward scan gate for MSW incineration AI belongs at every rendered-image ingestion boundary in the WTE combustion control and emissions monitoring pipeline — before combustion grate FLIR camera AI processes rendered false-colour grate thermal images, before CEMS opacity display AI processes rendered opacity trend and readout images, before bottom ash quench pit camera AI processes rendered ash discharge surveillance images, and before waste feed hopper level camera AI processes rendered hopper interior images. Threshold 35 for MSW incineration AI contexts reflects the consequence envelope of MACT Subpart CCCC dioxin/furan TEQ exceedance (13 ng TEQ/dscm new; 60 ng TEQ/dscm existing) driven by suppressed combustion cold zone detection, CAA Section 113 civil penalties, bottom ash conveyor fire with deflagration risk in enclosed ash galleries, and RCRA D002 corrosivity reclassification from abnormal ash chemistry.

import asyncio, base64, hashlib, json
from datetime import datetime, timezone
from enum import Enum
from pathlib import Path

import httpx

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

# MSW incineration AI contexts: threshold 35
# EPA MACT Subpart CCCC (40 CFR Part 63, Large Municipal Waste Combustors);
# Clean Air Act Section 129 (solid waste combustion standards);
# RCRA Subtitle D (non-hazardous solid waste); RCRA D002 (corrosivity).
MSW_INCINERATION_THRESHOLD = 35


class MSWIncinerationAIContext(Enum):
    GRATE_CAMERA      = "grate_camera"       # Combustion grate FLIR camera AI
    CEMS_OPACITY      = "cems_opacity"        # Stack opacity CEMS display AI
    BOTTOM_ASH        = "bottom_ash"          # Bottom ash quench camera AI
    WASTE_FEED_HOPPER = "waste_feed_hopper"   # Waste feed hopper level AI


class AdversarialMSWIncinerationImageError(Exception):
    """Raised when Glyphward detects adversarial content in a MSW
    incineration AI rendered image above threshold 35.

    Consequence if not raised:
    - GRATE_CAMERA: cold zone suppressed → CO spike above 100 ppm MACT
      operating limit → de novo PCDD/PCDF synthesis at 250-600°C →
      MACT Subpart CCCC dioxin/furan TEQ exceedance.
    - CEMS_OPACITY: opacity exceedance suppressed → excess emission
      report not generated → CAA §113 knowing violation risk.
    - BOTTOM_ASH: red ash on conveyor suppressed → conveyor fire in
      enclosed ash gallery → deflagration risk; RCRA D002 reclassification.
    - WASTE_FEED_HOPPER: empty hopper suppressed → air ingress →
      O2 surge → grate bar overheating → grate fracture; NOx MACT spike.
    Fail-safe: halt MSW incineration AI classification; require manual
    grate inspection / CEMS data review before resuming AI-driven
    combustion control.
    """

    def __init__(self, scan_id: str, score: int,
                 context: MSWIncinerationAIContext,
                 plant_id: str, unit_id: str,
                 flagged_region: dict | None = None) -> None:
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.plant_id = plant_id
        self.unit_id = unit_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial MSW incineration image: "
            f"context={context.value} score={score} "
            f"plant={plant_id} unit={unit_id} scan_id={scan_id}"
        )


async def scan_msw_incineration_image(
    image_bytes: bytes,
    context: MSWIncinerationAIContext,
    plant_id: str,
    unit_id: str,
    client: httpx.AsyncClient,
) -> dict:
    """Scan an MSW incineration AI rendered image for adversarial content.

    Fail-safe contract: AdversarialMSWIncinerationImageError or httpx error →
    halt MSW incineration AI classification; require manual grate inspection
    (GRATE_CAMERA), CEMS transmissometer data review (CEMS_OPACITY), manual
    ash discharge inspection (BOTTOM_ASH), or manual hopper level check
    (WASTE_FEED_HOPPER) before resuming AI-driven combustion management.
    """
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"msw_incineration:{context.value}:{plant_id}:{unit_id}",
        "metadata": {
            "plant_id": plant_id,
            "unit_id": unit_id,
            "context": context.value,
            "image_sha256": image_hash,
        },
    }
    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"] > MSW_INCINERATION_THRESHOLD:
        raise AdversarialMSWIncinerationImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            plant_id=plant_id,
            unit_id=unit_id,
            flagged_region=result.get("flagged_region"),
        )
    return result

Deploy scan_msw_incineration_image at each MSW incineration AI rendered-image ingestion boundary: before combustion grate FLIR camera AI (threshold 35), before CEMS opacity display AI (threshold 35), before bottom ash quench pit camera AI (threshold 35), and before waste feed hopper level camera AI (threshold 35). On AdversarialMSWIncinerationImageError for GRATE_CAMERA context: immediately increase primary underfire air flow to the cold zone section, activate auxiliary support burners, and reduce grate stroke rate to extend residence time before resuming AI-driven combustion optimization. See also: chemical plant process safety AI prompt injection (related combustion process safety AI adversarial context) and energy utilities AI adversarial images (related thermal process AI adversarial injection overview). Get early access

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