Refinery Decarbonization, Economics and Petrochemical Evaluation

[Book navigation](../index.html) · [Assessment](../assessments/unit-08.html) · [Applied project](../projects/unit-08.html)

Unit target

Assess process performance and change decisions under catalyst degradation, fouling, safety constraints and carbon scenarios.

Engineering context

Equipment behavior changes over time. Catalyst activity and heat-transfer resistance are dynamic states, while reliability, shutdowns and operating envelopes determine realized capacity. Models developed from synthetic data are useful for screening and sensitivity, never evidence of asset-specific fitness or safe operating limits. The modeler should decide what constitutes a material stream, what was measured directly, and which quantities follow from an equation. Whenever possible, start with an inlet-and-outlet balance in physical units rather than with an index or a normalized graph. An index can be useful for a classroom sensitivity exercise, but it can also conceal the absolute process magnitude needed to estimate utilities, storage or emissions.

Decision boundary

Build decisions that survive uncertainty and identify which plant measurements are needed to replace the instructional proxy with verified equipment data. A good recommendation distinguishes what is established by conservation from what depends on kinetic, thermodynamic or operating assumptions. Record your data source and test method. Identify unknown parameters before asserting any performance. If two alternatives seem very close numerically, compute the range of results that plausible uncertainty could produce rather than picking the one with more decimals.

Technical study sequence

1. [Catalyst activity and regeneration cycles](../lessons/unit-08-lesson-01.html) — [interactive lab 22](../labs/lab-22.html)
2. [Fouling, pressure loss and cleaning economics](../lessons/unit-08-lesson-02.html) — [interactive lab 23](../labs/lab-23.html)
3. [Safety, reliability and change control](../lessons/unit-08-lesson-03.html) — [interactive lab 24](../labs/lab-24.html)
4. [Carbon-intensity scenarios and transition tradeoffs](../lessons/unit-08-lesson-04.html) — [interactive lab 24](../labs/lab-24.html)

Laboratory strategy

These three distinct models illuminate different physics or accounting relationships in the unit: Catalyst activity decay, Exchanger fouling loss, Capture-penalty carbon scenario. Take each model as a conditional hypothesis. For one model, reconstruct the default answer from the equation; for a second, find a limiting case; for the third, propose a measurement that could disprove it. A smooth trend is not an indicator of industrial accuracy, and sensitivity does not establish causality when the underlying variables are correlated.

Deliverable and independent scrutiny

Prepare a two-page engineering memo with a material and energy boundary, one calculation table, at least two uncertainty or failure scenarios, and a decision that names the data still needed. Use the [16 question assessment](../assessments/unit-08.html) to identify technical gaps. The [applied project](../projects/unit-08.html) then adds constraints from adjacent units so that a local process improvement can be evaluated for its plant-wide consequence.

Unit 08 · Lesson 01 — Catalyst activity and regeneration cycles

[Unit map](../units/unit-08.html) · [Open numerical laboratory](../labs/lab-22.html) · [Assessment](../assessments/unit-08.html) · [Applied project](../projects/unit-08.html)

The engineering problem

Catalysts age through coking, poisoning, sintering or loss of active surface, and different mechanisms imply different regeneration strategies. Trend analysis requires repeated comparable measurements because feed and temperature changes can mimic activity decline. Cycle length is a reliability and economic decision, not just a kinetic rate measurement.

The practical starting point is not the equation by itself; it is a description of the process boundary. Write down the feed, product, recovered co-product, waste and possible accumulation terms before choosing an indicator. In this lesson, the title catalyst activity and regeneration cycles indicates a specific decision lens within process evaluation. Its purpose is to connect a measured or assumed characteristic to an operational consequence with a statement of what would prove the link. Separate primary evidence, indirect proxy and calculated prediction; those three categories are not interchangeable.

Quantity definition and model reasoning

A teaching exponential activity model a(t)=exp(−kdt) can modify a plug-flow surrogate X(t)=1−exp(−k0 a(t)τ). This shows declining conversion at fixed residence time but cannot forecast specific catalyst lifetime without calibration.

To audit a model, begin with units. Write every mass flow on a common basis such as kg/h, every species flow on a consistent molar basis, and every energy rate as kW or MJ/h. Converting a component fraction from molar to mass basis requires component molar masses. A dimensionless index can be compared across scenarios only if the reference conditions and denominator have not changed. When the result is a percentage, specify percentage of what: total feed mass, converted feed, recovered product, sulfur atoms, or an observed time interval. The wrong denominator can change the interpretation even when the arithmetic is correct.

One useful way to check the numerical relationship is to inspect its limiting behavior. At zero conversion or removal, no product can appear or disappear through that mechanism; at complete removal, the tracked contaminant should be accounted for in another stream; at a cutpoint moved above or below the stated assay range, extrapolation should be rejected. A monotonic curve may be mathematically well behaved yet physically incomplete if secondary reactions, equilibrium or equipment capacity create a turning point outside the model.

Worked investigation: hypothetical data

For k0τ=2 at fresh catalyst condition and deactivation constant kd=0.02 per operating day, compare conversion at day 0 and day 60.

Worked answer and interpretation

Fresh severity k₀τ=2 gives X₀=1−exp(−2)≈86.47%. After 60 days at kd=0.02/day, activity a=exp(−1.2)≈0.301, and conversion X₆₀=1−exp(−2×0.301)≈45.23%. This smooth decline is not a specific catalyst-life prediction: actual mechanism and normalized rate data would be required.

Make a compact worksheet with four rows: starting assumptions, symbolic equation, numerical substitution and mass/energy/quality consistency check. Always place the units in the substitution row. An answer with four significant figures may be mathematically reproducible but should not be presented as a plant forecast when the assumed feed is only known roughly. Repeat the worksheet with one changed input, quantify the absolute and percentage change in the output, and ask which upstream measurement would be needed to defend that perturbation.

For numerical work, explicitly mark the inputs in this lesson as synthetic instructional values, including any yield factor, catalyst rate, representative boiling temperature or solvent efficiency. They must not be used as operating limits. If you wish to compare with published performance, define an independent, dated and source-specific dataset and evaluate whether its units and stream definitions match those of the exercise.

Plant integration and operating evidence

Use normalized activity indices and confirm analytical and operating measurement conditions. Evaluate regeneration risk, yield penalties, outage costs and constraints on feed contaminants. Seek signs of irreversible damage before scheduling cycle strategies.

Place the result in a real flowsheet context. A trend in a monitored parameter is not automatically proof of a root cause. Separate operating changes, feed variability, instrument drift and equipment deterioration. A safe decision requires independent engineering review and verified operating limits, never extrapolation of classroom curves. Ask where the upstream stream comes from, which downstream unit receives it, and how a change would shift at least one other required resource. A product-quality improvement might raise utility demand; more extensive contaminant removal might increase solvent regeneration; higher conversion may move load to gas compression or wastewater treatment. Draw the additional arrows on the block flow diagram, and do not claim that the local calculation has optimized the full plant.

For an evidence-based evaluation, design a measurement table with columns for variable, unit, sample or instrument location, time basis, quality method, expected repeatability and action on discrepancy. Include one direct process variable, one product property and one integration variable. A useful model-validation report should show residuals or errors for several cases, not only agreement at the nominal case used to choose parameters. Record potential confounding changes in feed, temperature, operator procedure and catalyst condition.

Assumption challenge and failure diagnosis

Regeneration cannot always restore fresh activity; oversimplified exponential decay hides step changes, poisoning thresholds and catalyst distribution.

A critical reader should ask which implicit assumptions were required to write the equation: steady state or dynamic accumulation, ideal mixing or plug flow, fixed or changing properties, homogeneous or multiphase composition, constant versus variable selectivity, and independence versus coupling of disturbances. Pick the assumptions that matter here. If a model fails, identify whether the defect originates in incorrect units, an invalid physical assumption, unmeasured variability, or numerical implementation. Merely adding complexity does not fix poor calibration data.

In practical terms, define a stop condition for inference: a measurement outside the sampled composition range, evidence of a new phase, an unexpected hazard, a negative concentration, or an implausible material imbalance invalidates the shortcut. Escalate to a validated model or an expert review before making a design or safety claim. The appropriate corrective action can be to collect data, not to alter equipment.

Numerical laboratory and reproducible evidence

The linked [interactive laboratory, Catalyst activity decay](../labs/lab-22.html), provides a separate numerical sensitivity exercise. Its governing relationship is a(t)=exp(−kd t); X=1−exp(−k0τ a). The laboratory is connected to this lesson through its unit-level concepts, but it may explore a different variable from the preceding worked example. Check its JSON parameter definitions, alter one input at a time and export the sampled curve to CSV. Compare two cases by recomputing the equation manually; document the difference between software correctness and physical validity.

Before declaring completion, articulate these four results in your own words: the stream or specimen under study, the dimension and reporting basis of the output, the reason for the sign of its sensitivity, and a specific observation that could disconfirm the model. Relate the lesson to Direct emissions and carbon inventories as prior context and Fouling, pressure loss and cleaning economics as a subsequent design issue. This linkage should be causal, not merely a sequence of chapter names.

Independent checkpoints

1. Sketch the process boundary for catalyst activity and regeneration cycles and label the stream or specimen basis that is actually measured.
2. Recreate the numerical reasoning from the worked investigation with all units and a limiting-case check.
3. Identify one operation, market specification or safety constraint that the formula does not contain.
4. Propose two measurements that would distinguish this model from a competing physical interpretation.
5. Describe why a correct plot of catalyst activity decay does not establish plant-scale validity.

Editorial status: This is a provisional university-level teaching draft. Peer review, equipment-specific validation and current local requirements remain outside this instructional package.

Unit 08 · Lesson 02 — Fouling, pressure loss and cleaning economics

[Unit map](../units/unit-08.html) · [Open numerical laboratory](../labs/lab-23.html) · [Assessment](../assessments/unit-08.html) · [Applied project](../projects/unit-08.html)

The engineering problem

Deposits reduce heat transfer and may increase pressure loss, potentially forcing higher furnace duty or limiting processing rate. Fouling mechanisms include inorganic salts, asphaltene precipitation, polymerization and corrosion products. Temperature-driven fouling can create feedback in which increased heat input accelerates deposition.

The practical starting point is not the equation by itself; it is a description of the process boundary. Write down the feed, product, recovered co-product, waste and possible accumulation terms before choosing an indicator. In this lesson, the title fouling, pressure loss and cleaning economics indicates a specific decision lens within process evaluation. Its purpose is to connect a measured or assumed characteristic to an operational consequence with a statement of what would prove the link. Separate primary evidence, indirect proxy and calculated prediction; those three categories are not interchangeable.

Quantity definition and model reasoning

A simple resistance model is 1/U = 1/Uclean + Rf. At a fixed area and representative temperature-driving force, Q = U A ΔT is a teaching comparison; rigorous exchangers require LMTD or segmented analysis and changing temperatures.

To audit a model, begin with units. Write every mass flow on a common basis such as kg/h, every species flow on a consistent molar basis, and every energy rate as kW or MJ/h. Converting a component fraction from molar to mass basis requires component molar masses. A dimensionless index can be compared across scenarios only if the reference conditions and denominator have not changed. When the result is a percentage, specify percentage of what: total feed mass, converted feed, recovered product, sulfur atoms, or an observed time interval. The wrong denominator can change the interpretation even when the arithmetic is correct.

One useful way to check the numerical relationship is to inspect its limiting behavior. At zero conversion or removal, no product can appear or disappear through that mechanism; at complete removal, the tracked contaminant should be accounted for in another stream; at a cutpoint moved above or below the stated assay range, extrapolation should be rejected. A monotonic curve may be mathematically well behaved yet physically incomplete if secondary reactions, equilibrium or equipment capacity create a turning point outside the model.

Worked investigation: hypothetical data

A clean exchanger has U=500 W/m²K and fouling resistance 0.001 m²K/W. Calculate the fouled U and percentage decrease in heat duty if area and ΔT are held constant.

Worked answer and interpretation

Clean resistance is 1/500=0.002 m²K/W. Added resistance 0.001 gives total 0.003, hence U_fouled≈333.3 W/m²K. At equal area and temperature driving force the predicted duty falls by 1−333.3/500≈33.3%. Real exchanger temperatures and pressure drop may change with the fouling.

Make a compact worksheet with four rows: starting assumptions, symbolic equation, numerical substitution and mass/energy/quality consistency check. Always place the units in the substitution row. An answer with four significant figures may be mathematically reproducible but should not be presented as a plant forecast when the assumed feed is only known roughly. Repeat the worksheet with one changed input, quantify the absolute and percentage change in the output, and ask which upstream measurement would be needed to defend that perturbation.

For numerical work, explicitly mark the inputs in this lesson as synthetic instructional values, including any yield factor, catalyst rate, representative boiling temperature or solvent efficiency. They must not be used as operating limits. If you wish to compare with published performance, define an independent, dated and source-specific dataset and evaluate whether its units and stream definitions match those of the exercise.

Plant integration and operating evidence

Trend temperature-corrected heat transfer and pressure loss rather than raw temperatures alone. Compare avoided utility cost with cleaning downtime, inspection needs and the risk of leak or mechanical damage.

Place the result in a real flowsheet context. A trend in a monitored parameter is not automatically proof of a root cause. Separate operating changes, feed variability, instrument drift and equipment deterioration. A safe decision requires independent engineering review and verified operating limits, never extrapolation of classroom curves. Ask where the upstream stream comes from, which downstream unit receives it, and how a change would shift at least one other required resource. A product-quality improvement might raise utility demand; more extensive contaminant removal might increase solvent regeneration; higher conversion may move load to gas compression or wastewater treatment. Draw the additional arrows on the block flow diagram, and do not claim that the local calculation has optimized the full plant.

For an evidence-based evaluation, design a measurement table with columns for variable, unit, sample or instrument location, time basis, quality method, expected repeatability and action on discrepancy. Include one direct process variable, one product property and one integration variable. A useful model-validation report should show residuals or errors for several cases, not only agreement at the nominal case used to choose parameters. Record potential confounding changes in feed, temperature, operator procedure and catalyst condition.

Assumption challenge and failure diagnosis

The added-resistance model cannot diagnose deposit chemistry or a safe cleaning method. Real fouling rates depend on local flow, temperature and chemistry.

A critical reader should ask which implicit assumptions were required to write the equation: steady state or dynamic accumulation, ideal mixing or plug flow, fixed or changing properties, homogeneous or multiphase composition, constant versus variable selectivity, and independence versus coupling of disturbances. Pick the assumptions that matter here. If a model fails, identify whether the defect originates in incorrect units, an invalid physical assumption, unmeasured variability, or numerical implementation. Merely adding complexity does not fix poor calibration data.

In practical terms, define a stop condition for inference: a measurement outside the sampled composition range, evidence of a new phase, an unexpected hazard, a negative concentration, or an implausible material imbalance invalidates the shortcut. Escalate to a validated model or an expert review before making a design or safety claim. The appropriate corrective action can be to collect data, not to alter equipment.

Numerical laboratory and reproducible evidence

The linked [interactive laboratory, Exchanger fouling loss](../labs/lab-23.html), provides a separate numerical sensitivity exercise. Its governing relationship is 1/U=1/Uclean+Rf; Q=UAΔT. The laboratory is connected to this lesson through its unit-level concepts, but it may explore a different variable from the preceding worked example. Check its JSON parameter definitions, alter one input at a time and export the sampled curve to CSV. Compare two cases by recomputing the equation manually; document the difference between software correctness and physical validity.

Before declaring completion, articulate these four results in your own words: the stream or specimen under study, the dimension and reporting basis of the output, the reason for the sign of its sensitivity, and a specific observation that could disconfirm the model. Relate the lesson to Catalyst activity and regeneration cycles as prior context and Safety, reliability and change control as a subsequent design issue. This linkage should be causal, not merely a sequence of chapter names.

Independent checkpoints

1. Sketch the process boundary for fouling, pressure loss and cleaning economics and label the stream or specimen basis that is actually measured.
2. Recreate the numerical reasoning from the worked investigation with all units and a limiting-case check.
3. Identify one operation, market specification or safety constraint that the formula does not contain.
4. Propose two measurements that would distinguish this model from a competing physical interpretation.
5. Describe why a correct plot of exchanger fouling loss does not establish plant-scale validity.

Editorial status: This is a provisional university-level teaching draft. Peer review, equipment-specific validation and current local requirements remain outside this instructional package.

Unit 08 · Lesson 03 — Safety, reliability and change control

[Unit map](../units/unit-08.html) · [Open numerical laboratory](../labs/lab-24.html) · [Assessment](../assessments/unit-08.html) · [Applied project](../projects/unit-08.html)

The engineering problem

Petroleum and petrochemical sites manage inventories of flammable, toxic and pressurized materials. Risk analysis joins hazard scenarios, independent protection, human factors and maintenance. Quantified probability is meaningful only when event definitions, data period and dependencies are explicit.

The practical starting point is not the equation by itself; it is a description of the process boundary. Write down the feed, product, recovered co-product, waste and possible accumulation terms before choosing an indicator. In this lesson, the title safety, reliability and change control indicates a specific decision lens within process evaluation. Its purpose is to connect a measured or assumed characteristic to an operational consequence with a statement of what would prove the link. Separate primary evidence, indirect proxy and calculated prediction; those three categories are not interchangeable.

Quantity definition and model reasoning

A conceptual event tree computes outcome probability by multiplying conditional probabilities along each branch; this requires defensible conditional values and no unexamined dependence assumptions. Reliability of a protective layer is not the same as availability of a sensor alone.

To audit a model, begin with units. Write every mass flow on a common basis such as kg/h, every species flow on a consistent molar basis, and every energy rate as kW or MJ/h. Converting a component fraction from molar to mass basis requires component molar masses. A dimensionless index can be compared across scenarios only if the reference conditions and denominator have not changed. When the result is a percentage, specify percentage of what: total feed mass, converted feed, recovered product, sulfur atoms, or an observed time interval. The wrong denominator can change the interpretation even when the arithmetic is correct.

One useful way to check the numerical relationship is to inspect its limiting behavior. At zero conversion or removal, no product can appear or disappear through that mechanism; at complete removal, the tracked contaminant should be accounted for in another stream; at a cutpoint moved above or below the stated assay range, extrapolation should be rejected. A monotonic curve may be mathematically well behaved yet physically incomplete if secondary reactions, equilibrium or equipment capacity create a turning point outside the model.

Worked investigation: hypothetical data

For a synthetic initiating frequency of 0.1 events/year, a conditional protection failure of 0.05 and a second independent failure of 0.1, calculate the illustrative event rate. Explain why independence must be justified.

Worked answer and interpretation

With illustrative initiating-event frequency 0.1/year and two conditionally independent protection failures of 0.05 and 0.10, the branch event frequency is 0.1×0.05×0.10=0.0005 events/year. That is not an observed rate or a certification; common causes and data uncertainty can invalidate the multiplication.

Make a compact worksheet with four rows: starting assumptions, symbolic equation, numerical substitution and mass/energy/quality consistency check. Always place the units in the substitution row. An answer with four significant figures may be mathematically reproducible but should not be presented as a plant forecast when the assumed feed is only known roughly. Repeat the worksheet with one changed input, quantify the absolute and percentage change in the output, and ask which upstream measurement would be needed to defend that perturbation.

For numerical work, explicitly mark the inputs in this lesson as synthetic instructional values, including any yield factor, catalyst rate, representative boiling temperature or solvent efficiency. They must not be used as operating limits. If you wish to compare with published performance, define an independent, dated and source-specific dataset and evaluate whether its units and stream definitions match those of the exercise.

Plant integration and operating evidence

Use structured process hazard analysis, operating procedures, management of change, mechanical integrity and competency checks. Establish site-specific safeguards and regulatory obligations before any process modification.

Place the result in a real flowsheet context. A trend in a monitored parameter is not automatically proof of a root cause. Separate operating changes, feed variability, instrument drift and equipment deterioration. A safe decision requires independent engineering review and verified operating limits, never extrapolation of classroom curves. Ask where the upstream stream comes from, which downstream unit receives it, and how a change would shift at least one other required resource. A product-quality improvement might raise utility demand; more extensive contaminant removal might increase solvent regeneration; higher conversion may move load to gas compression or wastewater treatment. Draw the additional arrows on the block flow diagram, and do not claim that the local calculation has optimized the full plant.

For an evidence-based evaluation, design a measurement table with columns for variable, unit, sample or instrument location, time basis, quality method, expected repeatability and action on discrepancy. Include one direct process variable, one product property and one integration variable. A useful model-validation report should show residuals or errors for several cases, not only agreement at the nominal case used to choose parameters. Record potential confounding changes in feed, temperature, operator procedure and catalyst condition.

Assumption challenge and failure diagnosis

A simple probability tree does not certify the integrity of safety systems. Small numerical risks can hide severe model uncertainty, systematic failure and common-cause dependencies.

A critical reader should ask which implicit assumptions were required to write the equation: steady state or dynamic accumulation, ideal mixing or plug flow, fixed or changing properties, homogeneous or multiphase composition, constant versus variable selectivity, and independence versus coupling of disturbances. Pick the assumptions that matter here. If a model fails, identify whether the defect originates in incorrect units, an invalid physical assumption, unmeasured variability, or numerical implementation. Merely adding complexity does not fix poor calibration data.

In practical terms, define a stop condition for inference: a measurement outside the sampled composition range, evidence of a new phase, an unexpected hazard, a negative concentration, or an implausible material imbalance invalidates the shortcut. Escalate to a validated model or an expert review before making a design or safety claim. The appropriate corrective action can be to collect data, not to alter equipment.

Numerical laboratory and reproducible evidence

The linked [interactive laboratory, Capture-penalty carbon scenario](../labs/lab-24.html), provides a separate numerical sensitivity exercise. Its governing relationship is Enet=Ebase(1−capture)+penalty·capture. The laboratory is connected to this lesson through its unit-level concepts, but it may explore a different variable from the preceding worked example. Check its JSON parameter definitions, alter one input at a time and export the sampled curve to CSV. Compare two cases by recomputing the equation manually; document the difference between software correctness and physical validity.

Before declaring completion, articulate these four results in your own words: the stream or specimen under study, the dimension and reporting basis of the output, the reason for the sign of its sensitivity, and a specific observation that could disconfirm the model. Relate the lesson to Fouling, pressure loss and cleaning economics as prior context and Carbon-intensity scenarios and transition tradeoffs as a subsequent design issue. This linkage should be causal, not merely a sequence of chapter names.

Independent checkpoints

1. Sketch the process boundary for safety, reliability and change control and label the stream or specimen basis that is actually measured.
2. Recreate the numerical reasoning from the worked investigation with all units and a limiting-case check.
3. Identify one operation, market specification or safety constraint that the formula does not contain.
4. Propose two measurements that would distinguish this model from a competing physical interpretation.
5. Describe why a correct plot of capture-penalty carbon scenario does not establish plant-scale validity.

Editorial status: This is a provisional university-level teaching draft. Peer review, equipment-specific validation and current local requirements remain outside this instructional package.

Unit 08 · Lesson 04 — Carbon-intensity scenarios and transition tradeoffs

[Unit map](../units/unit-08.html) · [Open numerical laboratory](../labs/lab-24.html) · [Assessment](../assessments/unit-08.html) · [Applied project](../projects/unit-08.html)

The engineering problem

Carbon mitigation options include energy efficiency, low-carbon electricity, hydrogen pathway changes, combustion capture and feed/product choices. A process-level claim needs a defined baseline and system boundary, and capital and operating penalties can offset apparent benefits. Captured carbon must be transported and permanently managed under relevant requirements if a permanent-removal claim is made.

The practical starting point is not the equation by itself; it is a description of the process boundary. Write down the feed, product, recovered co-product, waste and possible accumulation terms before choosing an indicator. In this lesson, the title carbon-intensity scenarios and transition tradeoffs indicates a specific decision lens within process evaluation. Its purpose is to connect a measured or assumed characteristic to an operational consequence with a statement of what would prove the link. Separate primary evidence, indirect proxy and calculated prediction; those three categories are not interchangeable.

Quantity definition and model reasoning

A simple direct-emissions proxy is Enet = Ebase(1−c)+Epenalty(c), where c is gross capture fraction and penalty reflects additional energy-related emissions. The result is a scenario calculator, not a life-cycle assessment or a carbon-credit certification.

To audit a model, begin with units. Write every mass flow on a common basis such as kg/h, every species flow on a consistent molar basis, and every energy rate as kW or MJ/h. Converting a component fraction from molar to mass basis requires component molar masses. A dimensionless index can be compared across scenarios only if the reference conditions and denominator have not changed. When the result is a percentage, specify percentage of what: total feed mass, converted feed, recovered product, sulfur atoms, or an observed time interval. The wrong denominator can change the interpretation even when the arithmetic is correct.

One useful way to check the numerical relationship is to inspect its limiting behavior. At zero conversion or removal, no product can appear or disappear through that mechanism; at complete removal, the tracked contaminant should be accounted for in another stream; at a cutpoint moved above or below the stated assay range, extrapolation should be rejected. A monotonic curve may be mathematically well behaved yet physically incomplete if secondary reactions, equilibrium or equipment capacity create a turning point outside the model.

Worked investigation: hypothetical data

A plant baseline is 100 tCO₂/h. A scenario captures 70% but adds 8 tCO₂/h via additional energy. Calculate the new direct emissions and avoided amount; discuss whether downstream product use has changed.

Worked answer and interpretation

Residual emissions after 70% gross capture are 100×0.30=30 t CO₂/h. Adding 8 t CO₂/h from auxiliary energy gives a new direct total of 38 t CO₂/h, or 62 t CO₂/h below the initial 100. Whether the intervention reduces lifecycle impacts additionally depends on upstream energy, transportation, storage and downstream product use.

Make a compact worksheet with four rows: starting assumptions, symbolic equation, numerical substitution and mass/energy/quality consistency check. Always place the units in the substitution row. An answer with four significant figures may be mathematically reproducible but should not be presented as a plant forecast when the assumed feed is only known roughly. Repeat the worksheet with one changed input, quantify the absolute and percentage change in the output, and ask which upstream measurement would be needed to defend that perturbation.

For numerical work, explicitly mark the inputs in this lesson as synthetic instructional values, including any yield factor, catalyst rate, representative boiling temperature or solvent efficiency. They must not be used as operating limits. If you wish to compare with published performance, define an independent, dated and source-specific dataset and evaluate whether its units and stream definitions match those of the exercise.

Plant integration and operating evidence

Compare reductions per unit product, include utilization rate and uncertainty in energy emissions factors, and distinguish actual avoided emissions from captured mass. Treat electricity and hydrogen sourcing as scenario-dependent.

Place the result in a real flowsheet context. A trend in a monitored parameter is not automatically proof of a root cause. Separate operating changes, feed variability, instrument drift and equipment deterioration. A safe decision requires independent engineering review and verified operating limits, never extrapolation of classroom curves. Ask where the upstream stream comes from, which downstream unit receives it, and how a change would shift at least one other required resource. A product-quality improvement might raise utility demand; more extensive contaminant removal might increase solvent regeneration; higher conversion may move load to gas compression or wastewater treatment. Draw the additional arrows on the block flow diagram, and do not claim that the local calculation has optimized the full plant.

For an evidence-based evaluation, design a measurement table with columns for variable, unit, sample or instrument location, time basis, quality method, expected repeatability and action on discrepancy. Include one direct process variable, one product property and one integration variable. A useful model-validation report should show residuals or errors for several cases, not only agreement at the nominal case used to choose parameters. Record potential confounding changes in feed, temperature, operator procedure and catalyst condition.

Assumption challenge and failure diagnosis

At high capture rates, energy demand and other emissions can dominate net benefit. Claims of zero emissions require complete scope accounting and independently verified measurement.

A critical reader should ask which implicit assumptions were required to write the equation: steady state or dynamic accumulation, ideal mixing or plug flow, fixed or changing properties, homogeneous or multiphase composition, constant versus variable selectivity, and independence versus coupling of disturbances. Pick the assumptions that matter here. If a model fails, identify whether the defect originates in incorrect units, an invalid physical assumption, unmeasured variability, or numerical implementation. Merely adding complexity does not fix poor calibration data.

In practical terms, define a stop condition for inference: a measurement outside the sampled composition range, evidence of a new phase, an unexpected hazard, a negative concentration, or an implausible material imbalance invalidates the shortcut. Escalate to a validated model or an expert review before making a design or safety claim. The appropriate corrective action can be to collect data, not to alter equipment.

Numerical laboratory and reproducible evidence

The linked [interactive laboratory, Capture-penalty carbon scenario](../labs/lab-24.html), provides a separate numerical sensitivity exercise. Its governing relationship is Enet=Ebase(1−capture)+penalty·capture. The laboratory is connected to this lesson through its unit-level concepts, but it may explore a different variable from the preceding worked example. Check its JSON parameter definitions, alter one input at a time and export the sampled curve to CSV. Compare two cases by recomputing the equation manually; document the difference between software correctness and physical validity.

Before declaring completion, articulate these four results in your own words: the stream or specimen under study, the dimension and reporting basis of the output, the reason for the sign of its sensitivity, and a specific observation that could disconfirm the model. Relate the lesson to Safety, reliability and change control as prior context and particle and sustainability process concepts as a subsequent design issue. This linkage should be causal, not merely a sequence of chapter names.

Independent checkpoints

1. Sketch the process boundary for carbon-intensity scenarios and transition tradeoffs and label the stream or specimen basis that is actually measured.
2. Recreate the numerical reasoning from the worked investigation with all units and a limiting-case check.
3. Identify one operation, market specification or safety constraint that the formula does not contain.
4. Propose two measurements that would distinguish this model from a competing physical interpretation.
5. Describe why a correct plot of capture-penalty carbon scenario does not establish plant-scale validity.

Editorial status: This is a provisional university-level teaching draft. Peer review, equipment-specific validation and current local requirements remain outside this instructional package.

Unit 08 applied project — Process evaluation

[Unit 08](../units/unit-08.html) · [Book projects](../index.html)

Commissioning question

A fictional integrated refinery-and-petrochemicals teaching facility is reviewing its process evaluation decisions after receiving evidence that the assumed nominal feed or operating condition is no longer reliable. Your task is to produce an engineering screening memo, not an equipment datasheet or operating instruction. The central problem is: Assess process performance and change decisions under catalyst degradation, fouling, safety constraints and carbon scenarios.

Equipment behavior changes over time. Catalyst activity and heat-transfer resistance are dynamic states, while reliability, shutdowns and operating envelopes determine realized capacity. Models developed from synthetic data are useful for screening and sensitivity, never evidence of asset-specific fitness or safe operating limits.

Synthetic case boundary

Start from a nominal notional feed of 1,000 mass units per day for a single material balance; choose its actual chemical meaning and reporting basis before calculations. Allocate an assumed 10% uncertainty to the most sensitive unmeasured characteristic. Do not silently convert the same number into tonnes, cubic metres, kmol and standard gas volume. If a composition is needed, invent and label a test vector whose fractions sum exactly to unity. Keep the upstream and downstream units visible in a block flow diagram.

For this unit, the four required technical workstreams are Catalyst activity and regeneration cycles, Fouling, pressure loss and cleaning economics, Safety, reliability and change control, Carbon-intensity scenarios and transition tradeoffs. Use the lesson descriptions to select what evidence exists and what has to be assumed. If your assumed inputs are internally inconsistent—for example, a product yield above converted material, more recovered hydrogen than contained hydrogen, or a heat exchanger duty exceeding its thermodynamic ceiling—correct the conceptual error rather than tuning an output.

Computational experiment

Run three independent unit laboratories: [lab 22: Catalyst activity decay](../labs/lab-22.html), [lab 23: Exchanger fouling loss](../labs/lab-23.html), [lab 24: Capture-penalty carbon scenario](../labs/lab-24.html). For each lab, define a base case, one credible perturbation and a stress case near the end of the control range. Record the JSON model identifier, all parameter values, model output, units and qualitative interpretation. Export CSV. Reconstruct at least two displayed values with an independent worksheet or script, and plot both the baseline and a sensitivity trajectory.

Then write an uncertainty section: distinguish numerical precision, parameter uncertainty, structural model uncertainty and sampling uncertainty. An exact identity can be extremely precise but may be irrelevant to a poorly sampled feed. Conversely, a fitted kinetic coefficient can capture a short operating period and fail when catalyst activity changes. Explain which uncertainties could reverse the ranking of proposed alternatives.

Whole-plant coordination

Build decisions that survive uncertainty and identify which plant measurements are needed to replace the instructional proxy with verified equipment data. Name at least one impact on a different department: sulfur or wastewater handling, hydrogen consumption, off-gas, steam and power, product specifications, instrumentation, emergency response or carbon accounting. A nominally favorable local change is not a positive overall result until its resource and product interactions are reconciled. Identify an affected stakeholder who owns the required measurement or approval.

Deliverables and scoring

1. Boundary diagram and data dictionary (20%): readable stream labels, units, basis, data provenance and calculation assumptions.

2. Calculation appendix (25%): auditable mass or energy balance, three lab experiments and independent numerical recomputation.

3. Validation design (20%): at least two measurements, instrument/assay location, measurement frequency and acceptance criteria.

4. Trade-off memorandum (20%): alternative scenarios, constraints, uncertainty range and a reasoned but conditional comparison.

5. Responsible practice (15%): safety and environmental exclusions, uncertainty about regulations, and an explicit statement that the work is not plant-commissioning advice.

Challenge extension

Suppose the feed characteristic that controls the main model suddenly moves beyond the laboratory range. Write a plan for collecting the minimum data that would permit responsible recalibration without risking an unsupported process change. Your memo should explain why extrapolation is not a substitute for evidence and should name a decision point where engineering review is mandatory. The strongest result may be a refusal to infer a safe or profitable configuration from insufficient information.

Interactive laboratory 15: Steam methane reformer stoichiometry

[Open lab in the browser](lab-15.html) · [Unit 05](../units/unit-05.html)

Engineering purpose

Predict hydrogen formation from the isolated primary SMR reaction using a capped conversion proxy. The selected input is intentionally a one-variable exploratory slice. Move one setting at a time, check whether the displayed rate or fraction remains physically meaningful, and compare the curve against the conservation requirements of the process. The exercise is designed to show sensitivity and falsifiability, not to choose equipment sizes or plant operating conditions.

Equations and units

CH4+H2O→CO+3H2; nH2=3 nCH4 X

The result is reported as kmol H₂/h. Entered quantities are documented in the associated JSON and visible controls. Distinguish this calculated quantity from plant measurements and from a standardized certificate-of-analysis property. Whenever the formula contains a fitted coefficient or lumped index, describe what experimental dataset would be needed for calibration.

Procedure and recorded evidence

1. Start from the displayed nominal controls and record the output with all units.
2. Change the primary parameter to one end of its range. Use the plot to identify the sign and approximate local slope; record both the input and output.
3. Return to nominal settings, vary the second parameter when available, and explain why the primary trend changes—or why the one-dimensional model prevents a conclusion.
4. Export the 81-point dataset. Plot it independently in a spreadsheet or numerical notebook; check the endpoint values by hand using the equation.
5. Test a limiting or boundary case. State what would make the equation physically inappropriate or numerically invalid, even when the software shows a smooth graph.

Investigation

Vary S/C and identify the difference between a stoichiometric requirement and an operating target. Compare your result with an independent arithmetic calculation. Report a mass-, species-, or energy-balance audit appropriate to the process rather than relying on numerical agreement alone.

Scientific boundary

Conversion proxy is not reaction equilibrium or safe carbon-formation prediction. The graph is not evidence of a measured plant result. The model omits material characteristics, uncertainty sources and coupled constraints that would be required in a licensed process simulation. A useful lab report has separate sections for model prediction, measurement requirements, likely failure modes and decisions that must be deferred until validation.

Interactive laboratory 16: Linear gasoline octane screen

[Open lab in the browser](lab-16.html) · [Unit 06](../units/unit-06.html)

Engineering purpose

Compute a volume-based linear screening value between two fictional gasoline blending components. The selected input is intentionally a one-variable exploratory slice. Move one setting at a time, check whether the displayed rate or fraction remains physically meaningful, and compare the curve against the conservation requirements of the process. The exercise is designed to show sensitivity and falsifiability, not to choose equipment sizes or plant operating conditions.

Equations and units

RONscreen=x RON_A+(1−x)RON_B

The result is reported as RON proxy. Entered quantities are documented in the associated JSON and visible controls. Distinguish this calculated quantity from plant measurements and from a standardized certificate-of-analysis property. Whenever the formula contains a fitted coefficient or lumped index, describe what experimental dataset would be needed for calibration.

Procedure and recorded evidence

1. Start from the displayed nominal controls and record the output with all units.
2. Change the primary parameter to one end of its range. Use the plot to identify the sign and approximate local slope; record both the input and output.
3. Return to nominal settings, vary the second parameter when available, and explain why the primary trend changes—or why the one-dimensional model prevents a conclusion.
4. Export the 81-point dataset. Plot it independently in a spreadsheet or numerical notebook; check the endpoint values by hand using the equation.
5. Test a limiting or boundary case. State what would make the equation physically inappropriate or numerically invalid, even when the software shows a smooth graph.

Investigation

Compare the marginal change of a 10 percentage point blend shift. Compare your result with an independent arithmetic calculation. Report a mass-, species-, or energy-balance audit appropriate to the process rather than relying on numerical agreement alone.

Scientific boundary

Actual blending octane is non-additive; never use for fuel certification. The graph is not evidence of a measured plant result. The model omits material characteristics, uncertainty sources and coupled constraints that would be required in a licensed process simulation. A useful lab report has separate sections for model prediction, measurement requirements, likely failure modes and decisions that must be deferred until validation.

Unit 08 assessment — Process evaluation

[Return to unit](../units/unit-08.html) · [Solutions rubric](../ASSESSMENT-RUBRIC.html)

Instructions. Answer all sixteen tasks. Use hand calculations or exported simulator data where appropriate. State your assumptions, units and the difference between data and hypothetical values. Results are not checked by automated grading.

Catalyst activity and regeneration cycles

Question 01 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — For k0τ=2 at fresh catalyst condition and deactivation constant kd=0.02 per operating day, compare conversion at day 0 and day 60. — write the defining equation from first principles or its stated empirical assumptions, substitute the quantities with explicit units, and compute a result and reasonable significant figures. Explain what precise physical quantity the answer represents. Then change one input by 10% and determine the approximate sensitivity, specifying whether a percentage or a percentage-point change is being discussed.

Question 02 — Test the conservation boundary

Draw an inlet–outlet boundary around the relevant operation for catalyst activity and regeneration cycles. Include feed, saleable product, recycle, off-gas, waste, accumulation and energy transfer where applicable. State a component or elemental balance that must be satisfied. Create a simple inconsistent dataset, quantify its imbalance and propose two concrete measurements that would help identify the error.

Question 03 — Challenge the physical model

Consider the assumption in the lesson that Regeneration cannot always restore fresh activity; oversimplified exponential decay hides step changes, poisoning thresholds and catalyst distribution. Construct a plausible case in which its limitation becomes important. Predict the direction of model bias if defensible, identify any sign that cannot be inferred, and design a laboratory or process-data check to distinguish an inadequate equation from a sensor or sampling error.

Question 04 — Prepare an operating evidence decision

Prepare a one-page decision memo linked to catalyst activity and regeneration cycles. Use this practice context: Use normalized activity indices and confirm analytical and operating measurement conditions. Evaluate regeneration risk, yield penalties, outage costs and constraints on feed contaminants. Seek signs of irreversible damage before scheduling cycle strategies. State one measurable target, two competing operational or design choices, one external constraint, and three data sources or instruments. Recommend only the next evidence-collection step; do not convert the classroom model into a real plant setpoint.

Fouling, pressure loss and cleaning economics

Question 05 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — A clean exchanger has U=500 W/m²K and fouling resistance 0.001 m²K/W. Calculate the fouled U and percentage decrease in heat duty if area and ΔT are held constant. — write the defining equation from first principles or its stated empirical assumptions, substitute the quantities with explicit units, and compute a result and reasonable significant figures. Explain what precise physical quantity the answer represents. Then change one input by 10% and determine the approximate sensitivity, specifying whether a percentage or a percentage-point change is being discussed.

Question 06 — Test the conservation boundary

Draw an inlet–outlet boundary around the relevant operation for fouling, pressure loss and cleaning economics. Include feed, saleable product, recycle, off-gas, waste, accumulation and energy transfer where applicable. State a component or elemental balance that must be satisfied. Create a simple inconsistent dataset, quantify its imbalance and propose two concrete measurements that would help identify the error.

Question 07 — Challenge the physical model

Consider the assumption in the lesson that The added-resistance model cannot diagnose deposit chemistry or a safe cleaning method. Real fouling rates depend on local flow, temperature and chemistry. Construct a plausible case in which its limitation becomes important. Predict the direction of model bias if defensible, identify any sign that cannot be inferred, and design a laboratory or process-data check to distinguish an inadequate equation from a sensor or sampling error.

Question 08 — Prepare an operating evidence decision

Prepare a one-page decision memo linked to fouling, pressure loss and cleaning economics. Use this practice context: Trend temperature-corrected heat transfer and pressure loss rather than raw temperatures alone. Compare avoided utility cost with cleaning downtime, inspection needs and the risk of leak or mechanical damage. State one measurable target, two competing operational or design choices, one external constraint, and three data sources or instruments. Recommend only the next evidence-collection step; do not convert the classroom model into a real plant setpoint.

Safety, reliability and change control

Question 09 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — For a synthetic initiating frequency of 0.1 events/year, a conditional protection failure of 0.05 and a second independent failure of 0.1, calculate the illustrative event rate. Explain why independence must be justified. — write the defining equation from first principles or its stated empirical assumptions, substitute the quantities with explicit units, and compute a result and reasonable significant figures. Explain what precise physical quantity the answer represents. Then change one input by 10% and determine the approximate sensitivity, specifying whether a percentage or a percentage-point change is being discussed.

Question 10 — Test the conservation boundary

Draw an inlet–outlet boundary around the relevant operation for safety, reliability and change control. Include feed, saleable product, recycle, off-gas, waste, accumulation and energy transfer where applicable. State a component or elemental balance that must be satisfied. Create a simple inconsistent dataset, quantify its imbalance and propose two concrete measurements that would help identify the error.

Question 11 — Challenge the physical model

Consider the assumption in the lesson that A simple probability tree does not certify the integrity of safety systems. Small numerical risks can hide severe model uncertainty, systematic failure and common-cause dependencies. Construct a plausible case in which its limitation becomes important. Predict the direction of model bias if defensible, identify any sign that cannot be inferred, and design a laboratory or process-data check to distinguish an inadequate equation from a sensor or sampling error.

Question 12 — Prepare an operating evidence decision

Prepare a one-page decision memo linked to safety, reliability and change control. Use this practice context: Use structured process hazard analysis, operating procedures, management of change, mechanical integrity and competency checks. Establish site-specific safeguards and regulatory obligations before any process modification. State one measurable target, two competing operational or design choices, one external constraint, and three data sources or instruments. Recommend only the next evidence-collection step; do not convert the classroom model into a real plant setpoint.

Carbon-intensity scenarios and transition tradeoffs

Question 13 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — A plant baseline is 100 tCO₂/h. A scenario captures 70% but adds 8 tCO₂/h via additional energy. Calculate the new direct emissions and avoided amount; discuss whether downstream product use has changed. — write the defining equation from first principles or its stated empirical assumptions, substitute the quantities with explicit units, and compute a result and reasonable significant figures. Explain what precise physical quantity the answer represents. Then change one input by 10% and determine the approximate sensitivity, specifying whether a percentage or a percentage-point change is being discussed.

Question 14 — Test the conservation boundary

Draw an inlet–outlet boundary around the relevant operation for carbon-intensity scenarios and transition tradeoffs. Include feed, saleable product, recycle, off-gas, waste, accumulation and energy transfer where applicable. State a component or elemental balance that must be satisfied. Create a simple inconsistent dataset, quantify its imbalance and propose two concrete measurements that would help identify the error.

Question 15 — Challenge the physical model

Consider the assumption in the lesson that At high capture rates, energy demand and other emissions can dominate net benefit. Claims of zero emissions require complete scope accounting and independently verified measurement. Construct a plausible case in which its limitation becomes important. Predict the direction of model bias if defensible, identify any sign that cannot be inferred, and design a laboratory or process-data check to distinguish an inadequate equation from a sensor or sampling error.

Question 16 — Prepare an operating evidence decision

Prepare a one-page decision memo linked to carbon-intensity scenarios and transition tradeoffs. Use this practice context: Compare reductions per unit product, include utilization rate and uncertainty in energy emissions factors, and distinguish actual avoided emissions from captured mass. Treat electricity and hydrogen sourcing as scenario-dependent. State one measurable target, two competing operational or design choices, one external constraint, and three data sources or instruments. Recommend only the next evidence-collection step; do not convert the classroom model into a real plant setpoint.