Process Risk, Environment and Lifecycle

Design objective: Separate consequence screening, emissions balances and resource tradeoffs from formal safety-case and permitting decisions.

Common teaching scenario: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.

Unit-level engineering decision

The essential problem in this unit is not memorizing equations. It is forming a defensible decision from incomplete evidence. Separate consequence screening, emissions balances and resource tradeoffs from formal safety-case and permitting decisions. The case studies are synthetic; they provide a transparent set of inputs that can be checked without access to an operating facility. Before a numerical conclusion is adopted, identify the required physical evidence, constraints, and operating modes. Make clear whether the outcome is a conceptual screening result, a preliminary design basis, a vendor-specified rating, or a compliance decision. The four lessons examine distinct aspects of this decision.

Structured lesson sequence

1. [Hazard scenarios and qualitative barriers](../lessons/unit-07-lesson-01.html)

Hazard review begins with substance hazards and deviations, not with a single numerical risk product. Governing equation: illustrative expected loss = assumed event frequency × assumed consequence. Numerical benchmark: A hypothetical 0.02/y event frequency and $100000 consequence imply $2000/y expected loss in a toy expected-value model. Decision question: Decide which scenario requires avoidance, engineering safeguards or further quantitative analysis.

2. [Emissions and pollution prevention](../lessons/unit-07-lesson-02.html)

Report upstream generation and removal separately; destruction efficiency is never a mass-balance free pass. Governing equation: M_emit = M_generated × (1−capture fraction). Numerical benchmark: For 12 t/y generated pollutant and 0.85 capture, a simplified model predicts 1.8 t/y residual emissions. Decision question: Compare prevention at source with downstream capture rather than assuming end-of-pipe treatment is always best.

3. [Energy and lifecycle boundary choices](../lessons/unit-07-lesson-03.html)

Carbon comparisons must specify utility emission factors, process boundaries and upstream exclusions. Governing equation: annual CO2e = annual energy × assumed emission factor. Numerical benchmark: A 1000 MWh/y electric load at an illustrative 0.4 tCO2e/MWh corresponds to 400 tCO2e/y. Decision question: State which system boundary drives the ranking and whether the ranking reverses under alternate factors.

4. [Management of change and safe handover](../lessons/unit-07-lesson-04.html)

Design changes can invalidate earlier specifications, safety reviews and equipment limits. Governing equation: change impact = downstream assumptions and drawings affected by a changed design basis. Numerical benchmark: A switch from water to solvent can affect seals, classification, venting, materials and emission estimates. Decision question: Trace one feed-property change through datasheets, PFD, P&ID, operating instructions and training.

Integrated design activity

Construct a compact design dossier for the following system: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. The dossier must contain a flowsheet or decision diagram, a variable and unit register, an assumption register, at least two checks based on first principles, a sensitivity chart from one of the interactive models, a documented failure case, and an explicit decision with a reversal condition. Begin with the minimum information needed to formulate the problem; do not assign invented precise values to unknown vendor or market inputs. When data is unavailable, use a marked synthetic scenario and report the evidence gap.

Evidence and model boundaries

Topic 1 — Hazard scenarios and qualitative barriers. Do not use the example to decide relief sizing, occupancy, fire protection or process safety compliance. The next evidence sources are hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP. Any screening estimate using illustrative expected loss = assumed event frequency × assumed consequence remains conditional until those inputs are independently confirmed. A graph can make sensitivity visible; it cannot supply absent physical evidence.

Topic 2 — Emissions and pollution prevention. Permits and limits require pollutant-specific, jurisdiction-specific calculation methods. The next evidence sources are measured emissions factors, stack tests, process chemistry, control-system efficiency. Any screening estimate using M_emit = M_generated × (1−capture fraction) remains conditional until those inputs are independently confirmed. A graph can make sensitivity visible; it cannot supply absent physical evidence.

Topic 3 — Energy and lifecycle boundary choices. Operational energy-only accounting is not a full lifecycle assessment. The next evidence sources are supplier and grid-specific emission factors, material inputs and waste processing data. Any screening estimate using annual CO2e = annual energy × assumed emission factor remains conditional until those inputs are independently confirmed. A graph can make sensitivity visible; it cannot supply absent physical evidence.

Topic 4 — Management of change and safe handover. Only authorized competent teams can assess changes to regulated hazardous processes. The next evidence sources are revision log, MOC procedure, signed design reviews and controlled drawings. Any screening estimate using change impact = downstream assumptions and drawings affected by a changed design basis remains conditional until those inputs are independently confirmed. A graph can make sensitivity visible; it cannot supply absent physical evidence.

Unit laboratories

• [Lab 19: Illustrative expected-loss sensitivity](../labs/lab-19.html) — Explore why risk priority needs both likelihood and consequences.. Verification: Doubling either positive factor doubles expected monetary loss in this toy model.

• [Lab 20: Generation, capture and residual emissions](../labs/lab-20.html) — Track where pollutant mass goes in a simplified generation/removal diagram.. Verification: Residual cannot be negative for nonnegative generated and fugitive emissions.

• [Lab 21: Scope-limited utility CO₂e comparison](../labs/lab-21.html) — Demonstrate sensitivity of operational-energy accounting to grid and fuel assumptions.. Verification: If electricity factor is zero, modeled electricity-related CO₂e must be zero.

Unit review protocol

For each model, run default and altered cases. Check the governing relation algebraically for one point; verify dimensional consistency and domain validity; identify at least one limiting case; export a CSV and preserve the selected parameter settings in the report. A smooth graph can still be wrong, so treat independent calculations and physical evidence as separate gates. Before using the result outside a classroom, request suitable measured inputs, professional design reviews and applicable approvals.

[16-question assessment](../assessments/unit-07.html) · [Applied project](../projects/unit-07.html) · [Book contents](../index.html)

Unit 7 · Lesson 1: Hazard scenarios and qualitative barriers

Learning pathway: [Process Risk, Environment and Lifecycle](../units/unit-07.html) · [Simulation 19: Illustrative expected-loss sensitivity](../labs/lab-19.html) · [Assessment](../assessments/unit-07.html).

Engineering problem and conceptual boundary

Hazard review begins with substance hazards and deviations, not with a single numerical risk product. The central design case for this part of the course is: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. Design work should begin by deciding what the process is required to accomplish, what physical behavior is known, what remains an assumption, and which constraints make a candidate concept unacceptable. A calculated number that is not connected to a defined engineering decision has little project value. For this lesson the decision is: Decide which scenario requires avoidance, engineering safeguards or further quantitative analysis.

Risk magnitude alone cannot certify acceptability or replace consequence-based safeguards. Interpret that physical observation as a condition on the model rather than as a measured result. A screening model can provide a reliable direction of sensitivity without providing a trustworthy absolute plant rating. Before applying a particular numerical result, identify its process boundary, time basis, component definition and operating mode. A rate evaluated under steady conditions cannot represent a transient upset unless accumulation and duration are modeled explicitly.

Governing relationship and dimensional basis

The principal starting relationship is illustrative expected loss = assumed event frequency × assumed consequence. Write out the quantities in this equation and ask which are independently specified and which are inferred from other assumptions. Check that both sides have compatible units. When an output is expressed as a fraction it must not be treated as a mass or molar rate without multiplying by an appropriate total flow. When a cost or duty is given per year, make the operating-hour and utilization basis visible; a nameplate rate alone does not produce an annual total.

For hazard scenarios and qualitative barriers, the model is deliberately formulated as a minimal relation. A relation can be algebraically correct but physically incomplete, because it omits phenomena that do not appear in its chosen boundary. The specific concern here is: Do not use the example to decide relief sizing, occupancy, fire protection or process safety compliance. In a design review, show the equation as an auditable expression and keep the evidence for its coefficients separate from the mathematical logic. If an assumed parameter later changes, the calculation may need to be revisited across the entire flowsheet rather than edited only in one table.

Worked engineering check

Illustrative case, not plant measurement: A hypothetical 0.02/y event frequency and $100000 consequence imply $2000/y expected loss in a toy expected-value model. Copy the stated inputs into a notebook, calculate the result independently using the governing expression and retain enough significant digits to detect a balance error. When you write the result, distinguish an exact identity within the model from a rounded numerical example. For example, an exact component balance can hold to floating-point tolerance, while an empirical estimate of reaction behavior may be uncertain by orders of magnitude if fitted kinetics are unavailable.

Explain why the chosen arithmetic is useful for hazard scenarios and qualitative barriers but not sufficient for a detailed design decision. The calculation provides a specific benchmark for checking the interactive lab, not a guarantee that the input values are representative. If an initial calculation produces a result that contradicts expected physical bounds—such as composition outside the feed range, negative absolute inventory, positive losses with a perfect collection assumption, or implausible unit conversions—do not tune inputs simply to improve the display. Revisit the boundary and the assumption that creates the inconsistency.

Laboratory investigation with a falsifiable prediction

Open [Simulation 19: Illustrative expected-loss sensitivity](../labs/lab-19.html). The implemented relation is Expected loss/y = assumed event frequency × assumed monetary consequence. Before moving a control, predict what will happen to the output when the dominant parameter increases. Move that parameter to a value roughly halfway between the default and one end of its permitted interval. Record the previous and revised numerical outputs, the units, and a one-sentence explanation based on the equation. Then test a second parameter that changes the trend or limits the conclusion.

Specific falsification task: Doubling either positive factor doubles expected monetary loss in this toy model. If the code violates this check, the appropriate result is a failing test and a documented defect, not an attractive screenshot. Investigation prompt: Explain why low expected loss cannot by itself justify accepting a catastrophic scenario. The numerical lab is intentionally small enough to trace by hand; it should stimulate a quantitative question about process behavior rather than replace a commercial flowsheet simulator.

A defensible design record

For this lesson, create a one-page calculation note with: a clearly stated objective, a simple process or equipment boundary, all specified inputs including source and units, the model equation, the worked numeric output, one independent check, at least two changes of a parameter, and a decision with a stated uncertainty. The immediate evidence package to acquire is: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP. Label synthetic data plainly. Do not call an illustrative scenario a site survey, a vendor quotation, a laboratory result or a market forecast.

The most important decision consequence is: Decide which scenario requires avoidance, engineering safeguards or further quantitative analysis. A useful design note therefore contains a condition under which the recommendation would reverse. For example, the preferred concept may change if an availability assumption falls, an energy price rises, a flow composition moves beyond a separation range, or the estimated installed cost is outside the available budget. A reviewer needs the reversal threshold or at least a targeted experiment, not an unconditional claim that the concept is optimal.

Failure modes, omissions and escalation

Failure mode 1 — invalid physical simplification: Do not use the example to decide relief sizing, occupancy, fire protection or process safety compliance. If a physical mechanism outside the model dominates the real process, numerical refinement cannot rescue the conclusion. Failure mode 2 — missing input provenance: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP Without that evidence, parameter values remain unverified. Failure mode 3 — misuse of precision: a software result displayed to four decimals may still originate in an order-of-magnitude engineering assumption. Accuracy is controlled by evidence, not the number of digits shown.

Applicability boundary: Not a hazard approval, process-risk assessment, consequence model or defensible frequency database. This educational module does not size pressure vessels, specify safety instrumented functions, certify emissions compliance, supply construction documents or establish investment attractiveness. For an actual facility, relevant professional review, regulatory analysis, vendor thermal and mechanical design, and empirical validation are separate requirements that cannot be delegated to this one equation.

Independent study and examiner prompts

Conceptual: In what circumstances does the assumed relationship for hazard scenarios and qualitative barriers lose predictive value? Name one physical effect and one organizational or data-management effect. Quantitative: Reproduce the worked case with units and change one input by ten percent. State the sensitivity of the output, including whether a finite-difference result agrees with the local algebraic trend. Application: Relate the result to the unit case—An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.—and identify the critical tradeoff between an apparently favorable result and its implementation.

Critical appraisal: Draft the most persuasive argument against acting on this lesson’s estimate. Your argument must refer to evidence rather than taste. It should include at least one unavailable measurement from this list: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP. Then give the smallest practical study—vendor request, sample test, pilot run, site visit or model comparison—that could narrow the uncertainty. Communication: Write a two-sentence summary that separates a verified equation from a synthetic scenario assumption.

Short calculation audit for reviewers

Check that the supplied equation illustrative expected loss = assumed event frequency × assumed consequence is the same one used in the laboratory, or state why the lab computes a related screening quantity rather than the entire model. Confirm that the active scenario does not violate stated slider ranges. Confirm that no unit conversion is silently required between mass flow, molar flow, volume, energy or currency. Separate conservation-law residuals from empirical-correlation fit errors. Keep the response to a failed check in the delivered report; an invalid baseline should not be silently replaced with a different input set.

Checklist for mastery

• I can define the process boundary and the measured or assumed quantities behind Hazard scenarios and qualitative barriers.
• I can reproduce the stated working example with units and without copying a displayed answer.
• I can name the main omitted mechanism: Do not use the example to decide relief sizing, occupancy, fire protection or process safety compliance.
• I can run and explain the numerical limit test: Doubling either positive factor doubles expected monetary loss in this toy model.
• I can distinguish a planning estimate from a mechanical, regulatory or safety approval.
• I can name the next evidence source: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP

[Unit 7 overview](../units/unit-07.html) · [Lab 19](../labs/lab-19.html) · [Unit 7 assessment](../assessments/unit-07.html) · [Book contents](../index.html)

Unit 7 · Lesson 2: Emissions and pollution prevention

Learning pathway: [Process Risk, Environment and Lifecycle](../units/unit-07.html) · [Simulation 20: Generation, capture and residual emissions](../labs/lab-20.html) · [Assessment](../assessments/unit-07.html).

Engineering problem and conceptual boundary

Report upstream generation and removal separately; destruction efficiency is never a mass-balance free pass. The central design case for this part of the course is: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. Design work should begin by deciding what the process is required to accomplish, what physical behavior is known, what remains an assumption, and which constraints make a candidate concept unacceptable. A calculated number that is not connected to a defined engineering decision has little project value. For this lesson the decision is: Compare prevention at source with downstream capture rather than assuming end-of-pipe treatment is always best.

Capture device failures, secondary waste, fugitive losses and species conversion are not represented. Interpret that physical observation as a condition on the model rather than as a measured result. A screening model can provide a reliable direction of sensitivity without providing a trustworthy absolute plant rating. Before applying a particular numerical result, identify its process boundary, time basis, component definition and operating mode. A rate evaluated under steady conditions cannot represent a transient upset unless accumulation and duration are modeled explicitly.

Governing relationship and dimensional basis

The principal starting relationship is M_emit = M_generated × (1−capture fraction). Write out the quantities in this equation and ask which are independently specified and which are inferred from other assumptions. Check that both sides have compatible units. When an output is expressed as a fraction it must not be treated as a mass or molar rate without multiplying by an appropriate total flow. When a cost or duty is given per year, make the operating-hour and utilization basis visible; a nameplate rate alone does not produce an annual total.

For emissions and pollution prevention, the model is deliberately formulated as a minimal relation. A relation can be algebraically correct but physically incomplete, because it omits phenomena that do not appear in its chosen boundary. The specific concern here is: Permits and limits require pollutant-specific, jurisdiction-specific calculation methods. In a design review, show the equation as an auditable expression and keep the evidence for its coefficients separate from the mathematical logic. If an assumed parameter later changes, the calculation may need to be revisited across the entire flowsheet rather than edited only in one table.

Worked engineering check

Illustrative case, not plant measurement: For 12 t/y generated pollutant and 0.85 capture, a simplified model predicts 1.8 t/y residual emissions. Copy the stated inputs into a notebook, calculate the result independently using the governing expression and retain enough significant digits to detect a balance error. When you write the result, distinguish an exact identity within the model from a rounded numerical example. For example, an exact component balance can hold to floating-point tolerance, while an empirical estimate of reaction behavior may be uncertain by orders of magnitude if fitted kinetics are unavailable.

Explain why the chosen arithmetic is useful for emissions and pollution prevention but not sufficient for a detailed design decision. The calculation provides a specific benchmark for checking the interactive lab, not a guarantee that the input values are representative. If an initial calculation produces a result that contradicts expected physical bounds—such as composition outside the feed range, negative absolute inventory, positive losses with a perfect collection assumption, or implausible unit conversions—do not tune inputs simply to improve the display. Revisit the boundary and the assumption that creates the inconsistency.

Laboratory investigation with a falsifiable prediction

Open [Simulation 20: Generation, capture and residual emissions](../labs/lab-20.html). The implemented relation is Residual emissions = generated mass × (1−capture efficiency). Before moving a control, predict what will happen to the output when the dominant parameter increases. Move that parameter to a value roughly halfway between the default and one end of its permitted interval. Record the previous and revised numerical outputs, the units, and a one-sentence explanation based on the equation. Then test a second parameter that changes the trend or limits the conclusion.

Specific falsification task: Residual cannot be negative for nonnegative generated and fugitive emissions. If the code violates this check, the appropriate result is a failing test and a documented defect, not an attractive screenshot. Investigation prompt: Compare reducing formation by 30% with increasing capture from 85% to 95%. The numerical lab is intentionally small enough to trace by hand; it should stimulate a quantitative question about process behavior rather than replace a commercial flowsheet simulator.

A defensible design record

For this lesson, create a one-page calculation note with: a clearly stated objective, a simple process or equipment boundary, all specified inputs including source and units, the model equation, the worked numeric output, one independent check, at least two changes of a parameter, and a decision with a stated uncertainty. The immediate evidence package to acquire is: measured emissions factors, stack tests, process chemistry, control-system efficiency. Label synthetic data plainly. Do not call an illustrative scenario a site survey, a vendor quotation, a laboratory result or a market forecast.

The most important decision consequence is: Compare prevention at source with downstream capture rather than assuming end-of-pipe treatment is always best. A useful design note therefore contains a condition under which the recommendation would reverse. For example, the preferred concept may change if an availability assumption falls, an energy price rises, a flow composition moves beyond a separation range, or the estimated installed cost is outside the available budget. A reviewer needs the reversal threshold or at least a targeted experiment, not an unconditional claim that the concept is optimal.

Failure modes, omissions and escalation

Failure mode 1 — invalid physical simplification: Permits and limits require pollutant-specific, jurisdiction-specific calculation methods. If a physical mechanism outside the model dominates the real process, numerical refinement cannot rescue the conclusion. Failure mode 2 — missing input provenance: measured emissions factors, stack tests, process chemistry, control-system efficiency Without that evidence, parameter values remain unverified. Failure mode 3 — misuse of precision: a software result displayed to four decimals may still originate in an order-of-magnitude engineering assumption. Accuracy is controlled by evidence, not the number of digits shown.

Applicability boundary: Fugitive and stack streams are simplified; captured pollutant goes to a secondary waste sink, not disappearance. This educational module does not size pressure vessels, specify safety instrumented functions, certify emissions compliance, supply construction documents or establish investment attractiveness. For an actual facility, relevant professional review, regulatory analysis, vendor thermal and mechanical design, and empirical validation are separate requirements that cannot be delegated to this one equation.

Independent study and examiner prompts

Conceptual: In what circumstances does the assumed relationship for emissions and pollution prevention lose predictive value? Name one physical effect and one organizational or data-management effect. Quantitative: Reproduce the worked case with units and change one input by ten percent. State the sensitivity of the output, including whether a finite-difference result agrees with the local algebraic trend. Application: Relate the result to the unit case—An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.—and identify the critical tradeoff between an apparently favorable result and its implementation.

Critical appraisal: Draft the most persuasive argument against acting on this lesson’s estimate. Your argument must refer to evidence rather than taste. It should include at least one unavailable measurement from this list: measured emissions factors, stack tests, process chemistry, control-system efficiency. Then give the smallest practical study—vendor request, sample test, pilot run, site visit or model comparison—that could narrow the uncertainty. Communication: Write a two-sentence summary that separates a verified equation from a synthetic scenario assumption.

Short calculation audit for reviewers

Check that the supplied equation M_emit = M_generated × (1−capture fraction) is the same one used in the laboratory, or state why the lab computes a related screening quantity rather than the entire model. Confirm that the active scenario does not violate stated slider ranges. Confirm that no unit conversion is silently required between mass flow, molar flow, volume, energy or currency. Separate conservation-law residuals from empirical-correlation fit errors. Keep the response to a failed check in the delivered report; an invalid baseline should not be silently replaced with a different input set.

Checklist for mastery

• I can define the process boundary and the measured or assumed quantities behind Emissions and pollution prevention.
• I can reproduce the stated working example with units and without copying a displayed answer.
• I can name the main omitted mechanism: Permits and limits require pollutant-specific, jurisdiction-specific calculation methods.
• I can run and explain the numerical limit test: Residual cannot be negative for nonnegative generated and fugitive emissions.
• I can distinguish a planning estimate from a mechanical, regulatory or safety approval.
• I can name the next evidence source: measured emissions factors, stack tests, process chemistry, control-system efficiency

[Unit 7 overview](../units/unit-07.html) · [Lab 20](../labs/lab-20.html) · [Unit 7 assessment](../assessments/unit-07.html) · [Book contents](../index.html)

Unit 7 · Lesson 3: Energy and lifecycle boundary choices

Learning pathway: [Process Risk, Environment and Lifecycle](../units/unit-07.html) · [Simulation 21: Scope-limited utility CO₂e comparison](../labs/lab-21.html) · [Assessment](../assessments/unit-07.html).

Engineering problem and conceptual boundary

Carbon comparisons must specify utility emission factors, process boundaries and upstream exclusions. The central design case for this part of the course is: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. Design work should begin by deciding what the process is required to accomplish, what physical behavior is known, what remains an assumption, and which constraints make a candidate concept unacceptable. A calculated number that is not connected to a defined engineering decision has little project value. For this lesson the decision is: State which system boundary drives the ranking and whether the ranking reverses under alternate factors.

Emission factors are time- and location-dependent and are assumptions unless sourced. Interpret that physical observation as a condition on the model rather than as a measured result. A screening model can provide a reliable direction of sensitivity without providing a trustworthy absolute plant rating. Before applying a particular numerical result, identify its process boundary, time basis, component definition and operating mode. A rate evaluated under steady conditions cannot represent a transient upset unless accumulation and duration are modeled explicitly.

Governing relationship and dimensional basis

The principal starting relationship is annual CO2e = annual energy × assumed emission factor. Write out the quantities in this equation and ask which are independently specified and which are inferred from other assumptions. Check that both sides have compatible units. When an output is expressed as a fraction it must not be treated as a mass or molar rate without multiplying by an appropriate total flow. When a cost or duty is given per year, make the operating-hour and utilization basis visible; a nameplate rate alone does not produce an annual total.

For energy and lifecycle boundary choices, the model is deliberately formulated as a minimal relation. A relation can be algebraically correct but physically incomplete, because it omits phenomena that do not appear in its chosen boundary. The specific concern here is: Operational energy-only accounting is not a full lifecycle assessment. In a design review, show the equation as an auditable expression and keep the evidence for its coefficients separate from the mathematical logic. If an assumed parameter later changes, the calculation may need to be revisited across the entire flowsheet rather than edited only in one table.

Worked engineering check

Illustrative case, not plant measurement: A 1000 MWh/y electric load at an illustrative 0.4 tCO2e/MWh corresponds to 400 tCO2e/y. Copy the stated inputs into a notebook, calculate the result independently using the governing expression and retain enough significant digits to detect a balance error. When you write the result, distinguish an exact identity within the model from a rounded numerical example. For example, an exact component balance can hold to floating-point tolerance, while an empirical estimate of reaction behavior may be uncertain by orders of magnitude if fitted kinetics are unavailable.

Explain why the chosen arithmetic is useful for energy and lifecycle boundary choices but not sufficient for a detailed design decision. The calculation provides a specific benchmark for checking the interactive lab, not a guarantee that the input values are representative. If an initial calculation produces a result that contradicts expected physical bounds—such as composition outside the feed range, negative absolute inventory, positive losses with a perfect collection assumption, or implausible unit conversions—do not tune inputs simply to improve the display. Revisit the boundary and the assumption that creates the inconsistency.

Laboratory investigation with a falsifiable prediction

Open [Simulation 21: Scope-limited utility CO₂e comparison](../labs/lab-21.html). The implemented relation is Annual CO₂e = energy × assumed energy-specific emission factor. Before moving a control, predict what will happen to the output when the dominant parameter increases. Move that parameter to a value roughly halfway between the default and one end of its permitted interval. Record the previous and revised numerical outputs, the units, and a one-sentence explanation based on the equation. Then test a second parameter that changes the trend or limits the conclusion.

Specific falsification task: If electricity factor is zero, modeled electricity-related CO₂e must be zero. If the code violates this check, the appropriate result is a failing test and a documented defect, not an attractive screenshot. Investigation prompt: Find the crossover electricity emissions factor relative to the hypothetical fuel route. The numerical lab is intentionally small enough to trace by hand; it should stimulate a quantitative question about process behavior rather than replace a commercial flowsheet simulator.

A defensible design record

For this lesson, create a one-page calculation note with: a clearly stated objective, a simple process or equipment boundary, all specified inputs including source and units, the model equation, the worked numeric output, one independent check, at least two changes of a parameter, and a decision with a stated uncertainty. The immediate evidence package to acquire is: supplier and grid-specific emission factors, material inputs and waste processing data. Label synthetic data plainly. Do not call an illustrative scenario a site survey, a vendor quotation, a laboratory result or a market forecast.

The most important decision consequence is: State which system boundary drives the ranking and whether the ranking reverses under alternate factors. A useful design note therefore contains a condition under which the recommendation would reverse. For example, the preferred concept may change if an availability assumption falls, an energy price rises, a flow composition moves beyond a separation range, or the estimated installed cost is outside the available budget. A reviewer needs the reversal threshold or at least a targeted experiment, not an unconditional claim that the concept is optimal.

Failure modes, omissions and escalation

Failure mode 1 — invalid physical simplification: Operational energy-only accounting is not a full lifecycle assessment. If a physical mechanism outside the model dominates the real process, numerical refinement cannot rescue the conclusion. Failure mode 2 — missing input provenance: supplier and grid-specific emission factors, material inputs and waste processing data Without that evidence, parameter values remain unverified. Failure mode 3 — misuse of precision: a software result displayed to four decimals may still originate in an order-of-magnitude engineering assumption. Accuracy is controlled by evidence, not the number of digits shown.

Applicability boundary: Emission factors are invented for teaching; excludes production of equipment, materials, upstream leakage and local grid realities. This educational module does not size pressure vessels, specify safety instrumented functions, certify emissions compliance, supply construction documents or establish investment attractiveness. For an actual facility, relevant professional review, regulatory analysis, vendor thermal and mechanical design, and empirical validation are separate requirements that cannot be delegated to this one equation.

Independent study and examiner prompts

Conceptual: In what circumstances does the assumed relationship for energy and lifecycle boundary choices lose predictive value? Name one physical effect and one organizational or data-management effect. Quantitative: Reproduce the worked case with units and change one input by ten percent. State the sensitivity of the output, including whether a finite-difference result agrees with the local algebraic trend. Application: Relate the result to the unit case—An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.—and identify the critical tradeoff between an apparently favorable result and its implementation.

Critical appraisal: Draft the most persuasive argument against acting on this lesson’s estimate. Your argument must refer to evidence rather than taste. It should include at least one unavailable measurement from this list: supplier and grid-specific emission factors, material inputs and waste processing data. Then give the smallest practical study—vendor request, sample test, pilot run, site visit or model comparison—that could narrow the uncertainty. Communication: Write a two-sentence summary that separates a verified equation from a synthetic scenario assumption.

Short calculation audit for reviewers

Check that the supplied equation annual CO2e = annual energy × assumed emission factor is the same one used in the laboratory, or state why the lab computes a related screening quantity rather than the entire model. Confirm that the active scenario does not violate stated slider ranges. Confirm that no unit conversion is silently required between mass flow, molar flow, volume, energy or currency. Separate conservation-law residuals from empirical-correlation fit errors. Keep the response to a failed check in the delivered report; an invalid baseline should not be silently replaced with a different input set.

Checklist for mastery

• I can define the process boundary and the measured or assumed quantities behind Energy and lifecycle boundary choices.
• I can reproduce the stated working example with units and without copying a displayed answer.
• I can name the main omitted mechanism: Operational energy-only accounting is not a full lifecycle assessment.
• I can run and explain the numerical limit test: If electricity factor is zero, modeled electricity-related CO₂e must be zero.
• I can distinguish a planning estimate from a mechanical, regulatory or safety approval.
• I can name the next evidence source: supplier and grid-specific emission factors, material inputs and waste processing data

[Unit 7 overview](../units/unit-07.html) · [Lab 21](../labs/lab-21.html) · [Unit 7 assessment](../assessments/unit-07.html) · [Book contents](../index.html)

Unit 7 · Lesson 4: Management of change and safe handover

Learning pathway: [Process Risk, Environment and Lifecycle](../units/unit-07.html) · [Simulation 22: Design-basis change traceability](../labs/lab-22.html) · [Assessment](../assessments/unit-07.html).

Engineering problem and conceptual boundary

Design changes can invalidate earlier specifications, safety reviews and equipment limits. The central design case for this part of the course is: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. Design work should begin by deciding what the process is required to accomplish, what physical behavior is known, what remains an assumption, and which constraints make a candidate concept unacceptable. A calculated number that is not connected to a defined engineering decision has little project value. For this lesson the decision is: Trace one feed-property change through datasheets, PFD, P&ID, operating instructions and training.

The change-impact expression is a traceability procedure, not a numeric predictor. Interpret that physical observation as a condition on the model rather than as a measured result. A screening model can provide a reliable direction of sensitivity without providing a trustworthy absolute plant rating. Before applying a particular numerical result, identify its process boundary, time basis, component definition and operating mode. A rate evaluated under steady conditions cannot represent a transient upset unless accumulation and duration are modeled explicitly.

Governing relationship and dimensional basis

The principal starting relationship is change impact = downstream assumptions and drawings affected by a changed design basis. Write out the quantities in this equation and ask which are independently specified and which are inferred from other assumptions. Check that both sides have compatible units. When an output is expressed as a fraction it must not be treated as a mass or molar rate without multiplying by an appropriate total flow. When a cost or duty is given per year, make the operating-hour and utilization basis visible; a nameplate rate alone does not produce an annual total.

For management of change and safe handover, the model is deliberately formulated as a minimal relation. A relation can be algebraically correct but physically incomplete, because it omits phenomena that do not appear in its chosen boundary. The specific concern here is: Only authorized competent teams can assess changes to regulated hazardous processes. In a design review, show the equation as an auditable expression and keep the evidence for its coefficients separate from the mathematical logic. If an assumed parameter later changes, the calculation may need to be revisited across the entire flowsheet rather than edited only in one table.

Worked engineering check

Illustrative case, not plant measurement: A switch from water to solvent can affect seals, classification, venting, materials and emission estimates. Copy the stated inputs into a notebook, calculate the result independently using the governing expression and retain enough significant digits to detect a balance error. When you write the result, distinguish an exact identity within the model from a rounded numerical example. For example, an exact component balance can hold to floating-point tolerance, while an empirical estimate of reaction behavior may be uncertain by orders of magnitude if fitted kinetics are unavailable.

Explain why the chosen arithmetic is useful for management of change and safe handover but not sufficient for a detailed design decision. The calculation provides a specific benchmark for checking the interactive lab, not a guarantee that the input values are representative. If an initial calculation produces a result that contradicts expected physical bounds—such as composition outside the feed range, negative absolute inventory, positive losses with a perfect collection assumption, or implausible unit conversions—do not tune inputs simply to improve the display. Revisit the boundary and the assumption that creates the inconsistency.

Laboratory investigation with a falsifiable prediction

Open [Simulation 22: Design-basis change traceability](../labs/lab-22.html). The implemented relation is Closure fraction = verified, approved requirements / all critical requirements. Before moving a control, predict what will happen to the output when the dominant parameter increases. Move that parameter to a value roughly halfway between the default and one end of its permitted interval. Record the previous and revised numerical outputs, the units, and a one-sentence explanation based on the equation. Then test a second parameter that changes the trend or limits the conclusion.

Specific falsification task: Closure fraction cannot exceed 100% and must decrease when unresolved requirements are added. If the code violates this check, the appropriate result is a failing test and a documented defect, not an attractive screenshot. Investigation prompt: Explain why closing ten low-criticality items cannot override an open high-severity hazard review. The numerical lab is intentionally small enough to trace by hand; it should stimulate a quantitative question about process behavior rather than replace a commercial flowsheet simulator.

A defensible design record

For this lesson, create a one-page calculation note with: a clearly stated objective, a simple process or equipment boundary, all specified inputs including source and units, the model equation, the worked numeric output, one independent check, at least two changes of a parameter, and a decision with a stated uncertainty. The immediate evidence package to acquire is: revision log, MOC procedure, signed design reviews and controlled drawings. Label synthetic data plainly. Do not call an illustrative scenario a site survey, a vendor quotation, a laboratory result or a market forecast.

The most important decision consequence is: Trace one feed-property change through datasheets, PFD, P&ID, operating instructions and training. A useful design note therefore contains a condition under which the recommendation would reverse. For example, the preferred concept may change if an availability assumption falls, an energy price rises, a flow composition moves beyond a separation range, or the estimated installed cost is outside the available budget. A reviewer needs the reversal threshold or at least a targeted experiment, not an unconditional claim that the concept is optimal.

Failure modes, omissions and escalation

Failure mode 1 — invalid physical simplification: Only authorized competent teams can assess changes to regulated hazardous processes. If a physical mechanism outside the model dominates the real process, numerical refinement cannot rescue the conclusion. Failure mode 2 — missing input provenance: revision log, MOC procedure, signed design reviews and controlled drawings Without that evidence, parameter values remain unverified. Failure mode 3 — misuse of precision: a software result displayed to four decimals may still originate in an order-of-magnitude engineering assumption. Accuracy is controlled by evidence, not the number of digits shown.

Applicability boundary: An issue-counting dashboard is not proof of safety or quality; critical hold points remain mandatory. This educational module does not size pressure vessels, specify safety instrumented functions, certify emissions compliance, supply construction documents or establish investment attractiveness. For an actual facility, relevant professional review, regulatory analysis, vendor thermal and mechanical design, and empirical validation are separate requirements that cannot be delegated to this one equation.

Independent study and examiner prompts

Conceptual: In what circumstances does the assumed relationship for management of change and safe handover lose predictive value? Name one physical effect and one organizational or data-management effect. Quantitative: Reproduce the worked case with units and change one input by ten percent. State the sensitivity of the output, including whether a finite-difference result agrees with the local algebraic trend. Application: Relate the result to the unit case—An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.—and identify the critical tradeoff between an apparently favorable result and its implementation.

Critical appraisal: Draft the most persuasive argument against acting on this lesson’s estimate. Your argument must refer to evidence rather than taste. It should include at least one unavailable measurement from this list: revision log, MOC procedure, signed design reviews and controlled drawings. Then give the smallest practical study—vendor request, sample test, pilot run, site visit or model comparison—that could narrow the uncertainty. Communication: Write a two-sentence summary that separates a verified equation from a synthetic scenario assumption.

Short calculation audit for reviewers

Check that the supplied equation change impact = downstream assumptions and drawings affected by a changed design basis is the same one used in the laboratory, or state why the lab computes a related screening quantity rather than the entire model. Confirm that the active scenario does not violate stated slider ranges. Confirm that no unit conversion is silently required between mass flow, molar flow, volume, energy or currency. Separate conservation-law residuals from empirical-correlation fit errors. Keep the response to a failed check in the delivered report; an invalid baseline should not be silently replaced with a different input set.

Checklist for mastery

• I can define the process boundary and the measured or assumed quantities behind Management of change and safe handover.
• I can reproduce the stated working example with units and without copying a displayed answer.
• I can name the main omitted mechanism: Only authorized competent teams can assess changes to regulated hazardous processes.
• I can run and explain the numerical limit test: Closure fraction cannot exceed 100% and must decrease when unresolved requirements are added.
• I can distinguish a planning estimate from a mechanical, regulatory or safety approval.
• I can name the next evidence source: revision log, MOC procedure, signed design reviews and controlled drawings

[Unit 7 overview](../units/unit-07.html) · [Lab 22](../labs/lab-22.html) · [Unit 7 assessment](../assessments/unit-07.html) · [Book contents](../index.html)

Applied design project 7: Process Risk, Environment and Lifecycle

Scenario: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.

Engineering goal: Separate consequence screening, emissions balances and resource tradeoffs from formal safety-case and permitting decisions.

Project brief and boundary

You are a junior design team preparing a conceptual recommendation for a supervised technical review. The project is not a request to produce construction-ready specifications. It asks whether a candidate concept merits a funded next study based on traceable evidence. Make one primary design decision linked to process risk, environment and lifecycle and identify both how it could fail and what information is needed before capital is committed. Design responsibility, commercial feasibility, safety, permit requirements and site-specific code compliance remain independent gate reviews.

Task A — Form a reproducible design basis

A1. Hazard scenarios and qualitative barriers: Hazard review begins with substance hazards and deviations, not with a single numerical risk product. Start from illustrative expected loss = assumed event frequency × assumed consequence and reproduce the benchmark: A hypothetical 0.02/y event frequency and $100000 consequence imply $2000/y expected loss in a toy expected-value model. Label numbers as synthetic, experimental, quoted or specified. Identify the actual evidence source required: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP.

A2. Emissions and pollution prevention: Report upstream generation and removal separately; destruction efficiency is never a mass-balance free pass. Start from M_emit = M_generated × (1−capture fraction) and reproduce the benchmark: For 12 t/y generated pollutant and 0.85 capture, a simplified model predicts 1.8 t/y residual emissions. Label numbers as synthetic, experimental, quoted or specified. Identify the actual evidence source required: measured emissions factors, stack tests, process chemistry, control-system efficiency.

A3. Energy and lifecycle boundary choices: Carbon comparisons must specify utility emission factors, process boundaries and upstream exclusions. Start from annual CO2e = annual energy × assumed emission factor and reproduce the benchmark: A 1000 MWh/y electric load at an illustrative 0.4 tCO2e/MWh corresponds to 400 tCO2e/y. Label numbers as synthetic, experimental, quoted or specified. Identify the actual evidence source required: supplier and grid-specific emission factors, material inputs and waste processing data.

A4. Management of change and safe handover: Design changes can invalidate earlier specifications, safety reviews and equipment limits. Start from change impact = downstream assumptions and drawings affected by a changed design basis and reproduce the benchmark: A switch from water to solvent can affect seals, classification, venting, materials and emission estimates. Label numbers as synthetic, experimental, quoted or specified. Identify the actual evidence source required: revision log, MOC procedure, signed design reviews and controlled drawings.

Task B — Build a traceable numerical investigation

[Lab 19 — Illustrative expected-loss sensitivity](../labs/lab-19.html): Explain why low expected loss cannot by itself justify accepting a catastrophic scenario. Validate the bound: Doubling either positive factor doubles expected monetary loss in this toy model. Export the CSV and annotate which equation limits the model. Do not claim that an illustrative spreadsheet predicts a real plant.

[Lab 20 — Generation, capture and residual emissions](../labs/lab-20.html): Compare reducing formation by 30% with increasing capture from 85% to 95%. Validate the bound: Residual cannot be negative for nonnegative generated and fugitive emissions. Export the CSV and annotate which equation limits the model. Do not claim that an illustrative spreadsheet predicts a real plant.

[Lab 21 — Scope-limited utility CO₂e comparison](../labs/lab-21.html): Find the crossover electricity emissions factor relative to the hypothetical fuel route. Validate the bound: If electricity factor is zero, modeled electricity-related CO₂e must be zero. Export the CSV and annotate which equation limits the model. Do not claim that an illustrative spreadsheet predicts a real plant.

Task C — Challenge the concept

Construct a short failure-first register with at least one physical failure, one commercial or cost failure, one operational difficulty and one missing-data risk. For process risk, environment and lifecycle, list the observable sign of each problem, the assumption it challenges and the cheapest credible test that could expose it. A risk register without an assigned next action or a stop/go criterion is not a useful project deliverable. Distinguish a design variation from an independent protection measure and seek qualified safety review where warranted.

Task D — Submit an engineering decision pack

The pack contains: (1) design-basis sheet; (2) flowsheet or concept comparison; (3) numbered stream and parameter tables with units; (4) independent hand checks; (5) controlled simulation figures and exported CSV; (6) constraints and exclusions; (7) cost or utility sensitivity where relevant; (8) failure and uncertainty register; (9) recommendation with what evidence would reverse it; (10) references and revision log. Document whether assumptions are invented for pedagogy, grounded in a published reference, requested from a supplier, or measured. A professionally reviewed design would need additional engineering discipline sign-offs.

Acceptance test

A reviewer unfamiliar with your work should be able to reproduce a benchmark from the unit, locate the 3 related labs, see why the governing equations are dimensionally consistent, find at least one intentionally adverse case, and identify an unresolved assumption that could invalidate the proposed decision. The case is: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios. All evidence obtained and not obtained should be distinguishable. Do not present an unresolved conceptual model as an approved site or plant design.

[Unit notes](../units/unit-07.html) · [Unit assessment](../assessments/unit-07.html)

Unit 7 assessment — Process Risk, Environment and Lifecycle

Context: An educational aqueous-product line with utility consumption, waste treatment and documented credible failure scenarios.

Answer all sixteen multi-part problems. All illustrative figures are synthetic. Include units, inputs, methods, checking and limitations. Each problem should use a different numerical or investigative test; do not replace a requested calculation with a qualitative assertion.

Multi-part questions

Lesson 1: Hazard scenarios and qualitative barriers

Q1 — First-principles reconstruction. Starting with the physical boundary discussed in Hazard scenarios and qualitative barriers, derive or justify illustrative expected loss = assumed event frequency × assumed consequence in the declared setting. Give every variable and its dimension. Reproduce the supplied check case: A hypothetical 0.02/y event frequency and $100000 consequence imply $2000/y expected loss in a toy expected-value model. Identify whether your result is a material balance, mathematical correlation, operating target or economic scenario. Explain why this classification controls how the result can be used.

Q2 — Perturbation study. In [Lab 19](../labs/lab-19.html), take the baseline parameter set, vary Assumed frequency through at least five values, and export the resulting CSV. Compute one finite-difference sensitivity from the tabulated points. Predict and explain the direction of change from the model equation before looking at the plotted curve. State when the apparent trend may cease to apply to actual equipment.

Q3 — Independent falsification. Test the assertion: Doubling either positive factor doubles expected monetary loss in this toy model. Choose one deliberately extreme combination permitted by the controls and report the result to at least three useful significant figures where meaningful. Explain whether a surprising output is a code defect, a breakdown of assumptions, or a valid mathematical limit. Do not ignore invalid or nonfinite outputs when an infeasible physical state is intentionally selected.

Q4 — Evidence and decision memo. Write a 200–300-word decision note addressing: Decide which scenario requires avoidance, engineering safeguards or further quantitative analysis. Contrast the synthetic lesson calculation with the following evidence still required for an operational claim: hazardous-substance inventory, credible scenarios, operator experience and professional HAZOP. Discuss this known limitation: Do not use the example to decide relief sizing, occupancy, fire protection or process safety compliance. Name a realistic first validation experiment or vendor request and explain which unfavorable result would change the decision.

Lesson 2: Emissions and pollution prevention

Q5 — First-principles reconstruction. Starting with the physical boundary discussed in Emissions and pollution prevention, derive or justify M_emit = M_generated × (1−capture fraction) in the declared setting. Give every variable and its dimension. Reproduce the supplied check case: For 12 t/y generated pollutant and 0.85 capture, a simplified model predicts 1.8 t/y residual emissions. Identify whether your result is a material balance, mathematical correlation, operating target or economic scenario. Explain why this classification controls how the result can be used.

Q6 — Perturbation study. In [Lab 20](../labs/lab-20.html), take the baseline parameter set, vary Pollutant generated through at least five values, and export the resulting CSV. Compute one finite-difference sensitivity from the tabulated points. Predict and explain the direction of change from the model equation before looking at the plotted curve. State when the apparent trend may cease to apply to actual equipment.

Q7 — Independent falsification. Test the assertion: Residual cannot be negative for nonnegative generated and fugitive emissions. Choose one deliberately extreme combination permitted by the controls and report the result to at least three useful significant figures where meaningful. Explain whether a surprising output is a code defect, a breakdown of assumptions, or a valid mathematical limit. Do not ignore invalid or nonfinite outputs when an infeasible physical state is intentionally selected.

Q8 — Evidence and decision memo. Write a 200–300-word decision note addressing: Compare prevention at source with downstream capture rather than assuming end-of-pipe treatment is always best. Contrast the synthetic lesson calculation with the following evidence still required for an operational claim: measured emissions factors, stack tests, process chemistry, control-system efficiency. Discuss this known limitation: Permits and limits require pollutant-specific, jurisdiction-specific calculation methods. Name a realistic first validation experiment or vendor request and explain which unfavorable result would change the decision.

Lesson 3: Energy and lifecycle boundary choices

Q9 — First-principles reconstruction. Starting with the physical boundary discussed in Energy and lifecycle boundary choices, derive or justify annual CO2e = annual energy × assumed emission factor in the declared setting. Give every variable and its dimension. Reproduce the supplied check case: A 1000 MWh/y electric load at an illustrative 0.4 tCO2e/MWh corresponds to 400 tCO2e/y. Identify whether your result is a material balance, mathematical correlation, operating target or economic scenario. Explain why this classification controls how the result can be used.

Q10 — Perturbation study. In [Lab 21](../labs/lab-21.html), take the baseline parameter set, vary Annual electricity through at least five values, and export the resulting CSV. Compute one finite-difference sensitivity from the tabulated points. Predict and explain the direction of change from the model equation before looking at the plotted curve. State when the apparent trend may cease to apply to actual equipment.

Q11 — Independent falsification. Test the assertion: If electricity factor is zero, modeled electricity-related CO₂e must be zero. Choose one deliberately extreme combination permitted by the controls and report the result to at least three useful significant figures where meaningful. Explain whether a surprising output is a code defect, a breakdown of assumptions, or a valid mathematical limit. Do not ignore invalid or nonfinite outputs when an infeasible physical state is intentionally selected.

Q12 — Evidence and decision memo. Write a 200–300-word decision note addressing: State which system boundary drives the ranking and whether the ranking reverses under alternate factors. Contrast the synthetic lesson calculation with the following evidence still required for an operational claim: supplier and grid-specific emission factors, material inputs and waste processing data. Discuss this known limitation: Operational energy-only accounting is not a full lifecycle assessment. Name a realistic first validation experiment or vendor request and explain which unfavorable result would change the decision.

Lesson 4: Management of change and safe handover

Q13 — First-principles reconstruction. Starting with the physical boundary discussed in Management of change and safe handover, derive or justify change impact = downstream assumptions and drawings affected by a changed design basis in the declared setting. Give every variable and its dimension. Reproduce the supplied check case: A switch from water to solvent can affect seals, classification, venting, materials and emission estimates. Identify whether your result is a material balance, mathematical correlation, operating target or economic scenario. Explain why this classification controls how the result can be used.

Q14 — Perturbation study. In [Lab 22](../labs/lab-22.html), take the baseline parameter set, vary Verified critical requirements through at least five values, and export the resulting CSV. Compute one finite-difference sensitivity from the tabulated points. Predict and explain the direction of change from the model equation before looking at the plotted curve. State when the apparent trend may cease to apply to actual equipment.

Q15 — Independent falsification. Test the assertion: Closure fraction cannot exceed 100% and must decrease when unresolved requirements are added. Choose one deliberately extreme combination permitted by the controls and report the result to at least three useful significant figures where meaningful. Explain whether a surprising output is a code defect, a breakdown of assumptions, or a valid mathematical limit. Do not ignore invalid or nonfinite outputs when an infeasible physical state is intentionally selected.

Q16 — Evidence and decision memo. Write a 200–300-word decision note addressing: Trace one feed-property change through datasheets, PFD, P&ID, operating instructions and training. Contrast the synthetic lesson calculation with the following evidence still required for an operational claim: revision log, MOC procedure, signed design reviews and controlled drawings. Discuss this known limitation: Only authorized competent teams can assess changes to regulated hazardous processes. Name a realistic first validation experiment or vendor request and explain which unfavorable result would change the decision.

Marking and evidence standards

Allocate 25% to correct model and dimensions, 25% to worked numeric results and residual checks, 25% to parameter investigation and plots, and 25% to evidence quality, limitations and communication. Submit calculations, CSV exports, a one-page design note and a clearly labeled outstanding-information list. Material that implies these synthetic results certify an industrial design should not receive full credit.

[Unit notes](../units/unit-07.html) · [Applied project](../projects/unit-07.html)