Process safety culture, leadership and metrics, and capstone: a process hazard analysis and safeguard review for a small chemical unit

Every major accident investigation in this course has found technical failures, but behind them lie organizational ones: warnings that were not acted on, budgets that cut maintenance and staff, procedures that drifted from practice, and leaders who measured the wrong things. This final lesson looks at process safety culture and leadership, how to tell whether a process safety program is really working, and how to bring the methods of the course together. It ends with a capstone exercise: a process hazard analysis and safeguard review for a small solvent recovery unit.

What process safety culture means

Process safety culture is the combination of shared values, beliefs and behaviors that determine how an organization treats major accident hazards. A strong culture does not depend on a single champion. It shows up in everyday choices: whether a supervisor stops a startup when a trip is not working, whether an engineer's recommendation to replace corroded piping is funded, and whether a near miss is reported and investigated or quietly forgotten. CCPS describes a set of core principles for a good process safety culture, including establishing process safety as a core value, providing strong leadership, maintaining a sense of vulnerability, empowering people to fulfill their responsibilities, deferring to expertise, ensuring open and effective communication, establishing a questioning and learning environment, fostering mutual trust and responding to concerns in a timely way.

The term normalization of deviance was coined by sociologist Diane Vaughan in her study of the 1986 Challenger space shuttle disaster. It describes how a small departure from the expected standard, once it does not cause harm, gradually becomes the new normal, until the margin of safety has been eroded without anyone deciding to accept the risk. Process plants are vulnerable to it because most deviations, such as a bypassed alarm or a leaking flange, do not lead to an accident immediately.

Leadership

After Texas City, an independent panel led by former US Secretary of State James Baker reviewed process safety at BP's US refineries. Its 2007 report found that BP had emphasized personal safety and had not established a positive, trusting and open process safety culture, and it called for leadership from the board and executive levels down. The lesson applies to any organization handling hazardous materials. Leaders set the culture by what they ask about, what they fund, what they reward and how they respond to bad news.

Measuring process safety performance

Lesson 1 introduced lagging indicators, such as Tier 1 and Tier 2 process safety events under API RP 754, and leading indicators, such as Tier 3 challenges to safety systems and Tier 4 measures of operating discipline and management system performance. A good dashboard uses a small number of indicators chosen for the site's major hazards, tracks them over time, and triggers action when they move the wrong way. Each indicator should be linked to a barrier from the site's bow-ties (Lesson 7) so that a change in the indicator says something about the health of a specific safeguard.

# A simple process safety dashboard with red/amber/green status (seeded data).
import random
random.seed(311)
indicators = {
    # name: (value, target, amber limit, higher_is_better)
    "SIF proof tests on time (%)":       (random.randint(85, 100), 100, 95, True),
    "Inspections overdue (%)":           (round(random.uniform(0, 8), 1), 0, 2, False),
    "Active bypasses":                   (random.randint(0, 6), 0, 2, False),
    "Temporary MOCs past expiry":        (random.randint(0, 5), 0, 1, False),
    "Alarm rate (per operator hour)":    (round(random.uniform(4, 14), 1), 6, 12, False),
    "Near misses reported this month":   (random.randint(2, 20), 10, 5, True),
}
for name, (v, target, amber, hib) in indicators.items():
    if hib:
        status = "GREEN" if v >= target else ("AMBER" if v >= amber else "RED")
    else:
        status = "GREEN" if v <= target else ("AMBER" if v <= amber else "RED")
    print(f"{name:<34} {v:>6}  target {target:>4}  {status}")

SIF proof tests on time (%)            93  target  100  RED
Inspections overdue (%)               2.1  target    0  RED
Active bypasses                         0  target    0  GREEN
Temporary MOCs past expiry              3  target    0  RED
Alarm rate (per operator hour)        8.7  target    6  AMBER
Near misses reported this month         6  target   10  AMBER

A dashboard like this is only useful if someone acts on it. Each red or amber result should have an owner and an action, and leaders should ask about the trend, not only the current value. A rising number of near miss reports can be a good sign: it often means people trust that reporting leads to learning rather than blame.

Societal risk and F–N curves

Lesson 7 introduced individual risk. For facilities that could harm many people at once, such as a plant near a residential area, companies and regulators also look at societal risk. An F–N curve plots the cumulative frequency F of events causing N or more fatalities against N on log scales. A criterion line, which slopes downward, shows that larger events must be less frequent to be tolerable. Curves above the line need risk reduction.

# Build F-N points from a list of scenarios (frequency per year, fatalities), illustrative.
scenarios = [(2e-4, 1), (5e-5, 3), (1e-5, 10), (2e-6, 30), (5e-7, 100)]
Ns = sorted({n for _, n in scenarios})
print(" N   F(N or more) /yr   criterion /yr   status")
for N in Ns:
    F = sum(f for f, n in scenarios if n >= N)
    criterion = 1e-3 / N        # illustrative criterion line with slope -1
    status = "above line" if F > criterion else "below line"
    print(f"{N:>3}   {F:.2e}          {criterion:.1e}        {status}")

 N   F(N or more) /yr   criterion /yr   status
  1   2.63e-04          1.0e-03        below line
  3   6.25e-05          3.3e-04        below line
 10   1.25e-05          1.0e-04        below line
 30   2.50e-06          3.3e-05        below line
100   5.00e-07          1.0e-05        below line

Capstone: process hazard analysis and safeguard review for a small solvent recovery unit

The capstone brings together the methods of the course. Larchmont Specialty Chemicals plans to add a small solvent recovery unit, Unit 200, to recover toluene from used solvent. Used toluene, containing about 10 percent water and dissolved residues, is fed to a 4 m³ steam-heated still, S-201. Vapor passes to a water-cooled condenser, E-202, and the condensed toluene drains to a receiver, then is pumped to the existing 30 m³ storage tank T-100. The still operates near atmospheric pressure with a vent to a flame arrester. Steam to the still reboiler is controlled by a temperature controller. T-100 has a level transmitter and a high-level alarm, and is in a dike.

Step 1: Process safety information and hazards

The team first collects process safety information: material hazards (toluene is flammable with a flash point near 4 °C and a lower flammable limit of about 1.1 percent; Lesson 3), design pressures of the still and receiver, relief device design basis, P&IDs and the operating limits. It then lists the main hazards: fire or explosion from a toluene release, overpressure of the still, overfill of T-100, toxic exposure to toluene vapor, and the hazard of unknown residues in the used solvent, which might include reactive or peroxide-forming impurities.

Step 2: Hazard identification

The team uses a HAZOP on three nodes: the still and reboiler, the condenser and receiver, and the transfer to T-100. Selected deviations and their causes, consequences and existing safeguards are shown below.

Step 3: LOPA of the key scenarios

Two scenarios are taken to LOPA: loss of condenser cooling leading to a toluene vapor release and fire with a possible fatality, and overfill of T-100 leading to a spill, vapor cloud and possible fatality. The target for a single-fatality scenario is 1 × 10⁻⁵ per year. The HAZOP listed several safeguards, but the team must check which are valid IPLs.

# LOPA for two Unit 200 scenarios, before and after proposed safeguards.
target = 1e-5
scenarios = {
    "Loss of condenser cooling": {
        "ie": 0.1,                               # cooling water pump trip, per year
        "existing": {},                          # vent arrester does not prevent release; rounds not an IPL
        "conditional": 0.5 * 0.1,                # ignition x occupancy/fatality
        "proposed": {"SIL 1 trip: low CW flow closes steam": 0.1,
                     "operator response to independent high vent temperature alarm": 0.1},
    },
    "Overfill of T-100": {
        "ie": 0.1,                               # level transmitter failure or missed stop
        "existing": {"dike (limits spread)": 0.01},
        "conditional": 0.3 * 0.1,
        "proposed": {"independent high-high level switch trips P-204 (SIL 1)": 0.1},
    },
}
for name, s in scenarios.items():
    f = s["ie"] * s["conditional"]
    for p in s["existing"].values():
        f *= p
    print(f"{name}: existing {f:.1e} /yr ->", "meets" if f <= target else f"gap x{f / target:.0f}")
    for label_, p in s["proposed"].items():
        f *= p
    print(f"   with proposed safeguards {f:.1e} /yr ->", "meets" if f <= target else f"gap x{f / target:.0f}")

Loss of condenser cooling: existing 5.0e-03 /yr -> gap x500
   with proposed safeguards 5.0e-05 /yr -> gap x5
Overfill of T-100: existing 3.0e-05 /yr -> gap x3
   with proposed safeguards 3.0e-06 /yr -> meets

The analysis shows that the condenser scenario has a gap of a factor of 500 with existing safeguards, because the flame arrester does not stop the release and operator rounds are not an IPL. A SIL 1 trip that shuts off steam on low cooling water flow, plus an independent high vent temperature alarm with operator response, closes the gap to a factor of 5, so a further measure is still needed: the team proposes a SIL 2 design for the trip, giving a further factor of ten, or an inherently safer change to limit steam supply to below the condenser's capacity. The T-100 overfill scenario meets the target once an independent high-high level switch is added; it does not depend on the existing level transmitter that might have caused the overfill, the Buncefield lesson from Lesson 2.

Step 4: Recommendations and the management system

A PHA is only as good as what happens afterwards. The team's recommendations are assigned owners and dates and linked to the management system elements covered in the course.