Industrial Reactor Selection and Design Decisions

Scope: Productivity, safety, scale-up, economic tradeoffs.

Competencies

By the end of this unit, a reader should translate an operating observation into a defensible model, solve a numerical case using consistent units, identify the most influential assumption, and explain what evidence would invalidate the prediction. All calculations are teaching examples unless separately identified as standards-compliant design.

Unit learning sequence

• Lesson 1: Productivity and space-time yield
• Lesson 2: Scale-up and transport limits
• Lesson 3: Process safety and hazard review
• Lesson 4: Economic comparison and verification

Applied design studio

Integrated reactor technology selection. Compare reactor options with quantified throughput, usable yield, energy, availability, and capital recovery. Provide a table of uncertainty ranges, process hazards, vendor questions, scale-up tests and a falsification plan for the recommended architecture.

Measurement and reporting quality

A credible laboratory report separates raw data, corrected data, numerical predictions and observations. Preserve the original concentration basis and initial conditions. Label all time axes and distinguish a measured temperature from an assumed constant. Provide an uncertainty interval or at least a sensitivity range for the parameter controlling the decision. For safe desk exercises synthetic data are acceptable if labeled as such. Avoid suggesting unsupervised hazardous experiments or relying on classroom calculations for relief sizing.

Lesson 1: Productivity and space-time yield

Read [full lesson](../lessons/unit-08-lesson-01.html). Core equation: STY = mol desired product/(reactor volume × time).

Lesson 2: Scale-up and transport limits

Read [full lesson](../lessons/unit-08-lesson-02.html). Core equation: Da = kτ for first-order kinetics.

Lesson 3: Process safety and hazard review

Read [full lesson](../lessons/unit-08-lesson-03.html). Core equation: Risk screening = consequence category × likelihood category (qualitative only).

Lesson 4: Economic comparison and verification

Read [full lesson](../lessons/unit-08-lesson-04.html). Core equation: Annualized cost = capital recovery + utilities + feed losses + maintenance.

Unit 8, Lesson 1 — Productivity and space-time yield

Scope: Reactor decisions. Prerequisites: chemical material balances, algebra, introductory calculus and unit conversions.

Physical model and meaning

Production rate must be reported on an explicit reactant/product basis; selectivity and separation effort matter alongside conversion. In the present topic, productivity and space-time yield is an engineering interpretation problem before it is an algebra problem. Write the species, the observable state, the system boundary, and the governing time or length scale. The underlying quantity should be measured or inferred on an explicit basis. A change in what is held constant—reactor volume, inlet molar flow, heat capacity or concentration basis—can change the correct equation even when the equipment looks similar.

Use the working relationship STY = mol desired product/(reactor volume × time). First identify the independent variable and the dependent response. State whether it represents a local rate, an integrated outcome, or a steady-state closure condition. Perform a dimensional check before substituting numbers. For a measured plant variable, distinguish an instrument reading from a property calculated by a constitutive expression. In particular, a model may be exact inside an idealization yet wrong when its boundary conditions fail.

Process context and a falsifiable claim

Compare a small high-rate reactor to a large high-conversion reactor using identical feed and production bases. A falsifiable statement predicts how the measured response changes under a controlled, safe perturbation. Select one operating variable and hold the competing causes approximately constant. Write down what should happen if the equation is valid, what opposite observation would reject the assumption, and what additional sensor or sampling measurement can separate the explanations. In kinetic work, a mismatch between prediction and measurement may reflect a bad rate law, nonuniform temperature, nonideal flow, unrecognized side reaction, or a sampling artefact. Check those mechanisms independently rather than adjusting one fitted parameter until the chart looks plausible.

The practical question is not simply whether a numerical answer can be calculated. It is whether the proposed intervention changes the relevant physical mechanism and whether the predicted magnitude is large relative to instrument uncertainty and normal process variation. A temperature variation, for example, may alter both kinetics and properties. A flow variation may change residence time, heat removal, and mixing simultaneously. A careful experiment identifies these confounders.

Worked engineering example

Given. A reactor produces 12 kmol h⁻¹ of saleable product in 8 m³ of reacting volume.

Solution. STY=12/8=1.5 kmol m⁻³ h⁻¹. At 90% on-stream fraction, the calendar-average STY is 1.35 kmol m⁻³ h⁻¹.

Interpretation. This numerical example is valid only within its stated assumptions and unit system. Carry at least two plausible significant digits through intermediate steps, then round to the resolution of the input evidence. If experimental observations differ, report the difference before modifying the calculation. Check that all flows are on comparable molar bases and that any time units in a rate constant are consistent with the residence or observation time used.

Four checks before accepting the result

1. Net product output. Identify exactly which measurement or modeling choice controls this feature. Explain its physical role, record the correct units, and consider whether an independent observation could confirm it. A single agreement at one operating point does not demonstrate that the feature is represented correctly across the entire range.

2. On-stream factor. Ask what would change if this effect were twice as important as assumed. A meaningful sensitivity calculation changes one parameter while keeping the process basis fixed, then reports both the numerical response and any violated constraint. Include the consequence for experiment design.

3. Separation recovery. Separate information actually measured from information embedded in a model. A fitted coefficient may compensate for an omitted physical mechanism. Check whether different candidate models predict measurably different responses over the available operating range.

4. Quality yield. Revisit the design decision after accounting for uncertainty. Where a limit is approached, an apparently precise result must not justify operating beyond validated data. Keep a record of the parameter set and the approximation used to reach each conclusion.

Limits and engineering judgement

An unusually high STY may conceal low on-stream availability or excessive downstream recycle. When this limitation is significant, do not conceal it in a footnote: label the specific prediction as a screening estimate and state what would be required for a validated design. A reactor choice depends on chemistry, phase behavior, heat exchange, material compatibility, sensor reliability and operator response. No single idealized expression can substitute for thermal-hazard testing, validated kinetic data or codes applicable to the actual facility.

Extension for the reader. Construct a two-case comparison: one case follows the stated ideal assumptions; the other changes the most credible violated assumption. Sketch the expected shape of the dependent variable versus the relevant input. If the shape is qualitatively different, propose an experiment that will tell the models apart. Document what new evidence would make you reverse the original recommendation.

Independent problems and answer guidance

Q1. For productivity and space-time yield, explain the effect of net product output on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate net product output as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For productivity and space-time yield, explain the effect of on-stream factor on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate on-stream factor as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For productivity and space-time yield, explain the effect of separation recovery on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate separation recovery as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For productivity and space-time yield, explain the effect of quality yield on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate quality yield as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Learning evidence

Submit a one-page assumptions register, unit-consistent calculation, sensitivity plot and model rejection test. The quality criterion is not a single memorized numerical answer: it is the ability to explain why the conclusion may change under an independently observable perturbation.

Unit 8, Lesson 2 — Scale-up and transport limits

Scope: Reactor decisions. Prerequisites: chemical material balances, algebra, introductory calculus and unit conversions.

Physical model and meaning

Dimensionless similarity can help but cannot match Reynolds, Damköhler, heat transfer and mixing simultaneously in every scale-up. In the present topic, scale-up and transport limits is an engineering interpretation problem before it is an algebra problem. Write the species, the observable state, the system boundary, and the governing time or length scale. The underlying quantity should be measured or inferred on an explicit basis. A change in what is held constant—reactor volume, inlet molar flow, heat capacity or concentration basis—can change the correct equation even when the equipment looks similar.

Use the working relationship Da = kτ for first-order kinetics. First identify the independent variable and the dependent response. State whether it represents a local rate, an integrated outcome, or a steady-state closure condition. Perform a dimensional check before substituting numbers. For a measured plant variable, distinguish an instrument reading from a property calculated by a constitutive expression. In particular, a model may be exact inside an idealization yet wrong when its boundary conditions fail.

Process context and a falsifiable claim

Estimate how a tenfold increase in diameter changes area-to-volume ratio and heat-removal capacity; identify what lab evidence is missing. A falsifiable statement predicts how the measured response changes under a controlled, safe perturbation. Select one operating variable and hold the competing causes approximately constant. Write down what should happen if the equation is valid, what opposite observation would reject the assumption, and what additional sensor or sampling measurement can separate the explanations. In kinetic work, a mismatch between prediction and measurement may reflect a bad rate law, nonuniform temperature, nonideal flow, unrecognized side reaction, or a sampling artefact. Check those mechanisms independently rather than adjusting one fitted parameter until the chart looks plausible.

The practical question is not simply whether a numerical answer can be calculated. It is whether the proposed intervention changes the relevant physical mechanism and whether the predicted magnitude is large relative to instrument uncertainty and normal process variation. A temperature variation, for example, may alter both kinetics and properties. A flow variation may change residence time, heat removal, and mixing simultaneously. A careful experiment identifies these confounders.

Worked engineering example

Given. A cylindrical tank diameter increases by factor 3 at constant geometry, while operating duty per volume stays fixed.

Solution. Volume scales by 3³=27 while external area scales by 3²=9; area per unit volume decreases by factor 3. Heat-removal capability does not scale automatically with production.

Interpretation. This numerical example is valid only within its stated assumptions and unit system. Carry at least two plausible significant digits through intermediate steps, then round to the resolution of the input evidence. If experimental observations differ, report the difference before modifying the calculation. Check that all flows are on comparable molar bases and that any time units in a rate constant are consistent with the residence or observation time used.

Four checks before accepting the result

1. Surface-to-volume scaling. Identify exactly which measurement or modeling choice controls this feature. Explain its physical role, record the correct units, and consider whether an independent observation could confirm it. A single agreement at one operating point does not demonstrate that the feature is represented correctly across the entire range.

2. Mixing energy. Ask what would change if this effect were twice as important as assumed. A meaningful sensitivity calculation changes one parameter while keeping the process basis fixed, then reports both the numerical response and any violated constraint. Include the consequence for experiment design.

3. Transport/kinetics separation. Separate information actually measured from information embedded in a model. A fitted coefficient may compensate for an omitted physical mechanism. Check whether different candidate models predict measurably different responses over the available operating range.

4. Dimensionless groups. Revisit the design decision after accounting for uncertainty. Where a limit is approached, an apparently precise result must not justify operating beyond validated data. Keep a record of the parameter set and the approximation used to reach each conclusion.

Limits and engineering judgement

Kinetic parameters taken from poorly mixed vessels can absorb transport effects and fail upon scaling. When this limitation is significant, do not conceal it in a footnote: label the specific prediction as a screening estimate and state what would be required for a validated design. A reactor choice depends on chemistry, phase behavior, heat exchange, material compatibility, sensor reliability and operator response. No single idealized expression can substitute for thermal-hazard testing, validated kinetic data or codes applicable to the actual facility.

Extension for the reader. Construct a two-case comparison: one case follows the stated ideal assumptions; the other changes the most credible violated assumption. Sketch the expected shape of the dependent variable versus the relevant input. If the shape is qualitatively different, propose an experiment that will tell the models apart. Document what new evidence would make you reverse the original recommendation.

Independent problems and answer guidance

Q1. For scale-up and transport limits, explain the effect of surface-to-volume scaling on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate surface-to-volume scaling as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For scale-up and transport limits, explain the effect of mixing energy on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate mixing energy as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For scale-up and transport limits, explain the effect of transport/kinetics separation on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate transport/kinetics separation as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For scale-up and transport limits, explain the effect of dimensionless groups on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate dimensionless groups as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Learning evidence

Submit a one-page assumptions register, unit-consistent calculation, sensitivity plot and model rejection test. The quality criterion is not a single memorized numerical answer: it is the ability to explain why the conclusion may change under an independently observable perturbation.

Unit 8, Lesson 3 — Process safety and hazard review

Scope: Reactor decisions. Prerequisites: chemical material balances, algebra, introductory calculus and unit conversions.

Physical model and meaning

A credible decision incorporates inherent safety, operability analysis, protective instrumentation and management of change. In the present topic, process safety and hazard review is an engineering interpretation problem before it is an algebra problem. Write the species, the observable state, the system boundary, and the governing time or length scale. The underlying quantity should be measured or inferred on an explicit basis. A change in what is held constant—reactor volume, inlet molar flow, heat capacity or concentration basis—can change the correct equation even when the equipment looks similar.

Use the working relationship Risk screening = consequence category × likelihood category (qualitative only). First identify the independent variable and the dependent response. State whether it represents a local rate, an integrated outcome, or a steady-state closure condition. Perform a dimensional check before substituting numbers. For a measured plant variable, distinguish an instrument reading from a property calculated by a constitutive expression. In particular, a model may be exact inside an idealization yet wrong when its boundary conditions fail.

Process context and a falsifiable claim

Build an upset-case matrix for feed loss, coolant loss, blocked outlet, sensor failure and impurity ingress. A falsifiable statement predicts how the measured response changes under a controlled, safe perturbation. Select one operating variable and hold the competing causes approximately constant. Write down what should happen if the equation is valid, what opposite observation would reject the assumption, and what additional sensor or sampling measurement can separate the explanations. In kinetic work, a mismatch between prediction and measurement may reflect a bad rate law, nonuniform temperature, nonideal flow, unrecognized side reaction, or a sampling artefact. Check those mechanisms independently rather than adjusting one fitted parameter until the chart looks plausible.

The practical question is not simply whether a numerical answer can be calculated. It is whether the proposed intervention changes the relevant physical mechanism and whether the predicted magnitude is large relative to instrument uncertainty and normal process variation. A temperature variation, for example, may alter both kinetics and properties. A flow variation may change residence time, heat removal, and mixing simultaneously. A careful experiment identifies these confounders.

Worked engineering example

Given. Cooling loss and high feed concentration are two separate credible deviations.

Solution. Record each initiating event, expected temperature/inventory effects, alarm response and independent safeguards. Do not infer their frequencies from the count of incidents in a short pilot campaign.

Interpretation. This numerical example is valid only within its stated assumptions and unit system. Carry at least two plausible significant digits through intermediate steps, then round to the resolution of the input evidence. If experimental observations differ, report the difference before modifying the calculation. Check that all flows are on comparable molar bases and that any time units in a rate constant are consistent with the residence or observation time used.

Four checks before accepting the result

1. Independent safeguards. Identify exactly which measurement or modeling choice controls this feature. Explain its physical role, record the correct units, and consider whether an independent observation could confirm it. A single agreement at one operating point does not demonstrate that the feature is represented correctly across the entire range.

2. Operating limits. Ask what would change if this effect were twice as important as assumed. A meaningful sensitivity calculation changes one parameter while keeping the process basis fixed, then reports both the numerical response and any violated constraint. Include the consequence for experiment design.

3. Hazard-identification records. Separate information actually measured from information embedded in a model. A fitted coefficient may compensate for an omitted physical mechanism. Check whether different candidate models predict measurably different responses over the available operating range.

4. Change control. Revisit the design decision after accounting for uncertainty. Where a limit is approached, an apparently precise result must not justify operating beyond validated data. Keep a record of the parameter set and the approximation used to reach each conclusion.

Limits and engineering judgement

Multiplying arbitrary ordinal risk scores does not make a probabilistic risk estimate. When this limitation is significant, do not conceal it in a footnote: label the specific prediction as a screening estimate and state what would be required for a validated design. A reactor choice depends on chemistry, phase behavior, heat exchange, material compatibility, sensor reliability and operator response. No single idealized expression can substitute for thermal-hazard testing, validated kinetic data or codes applicable to the actual facility.

Extension for the reader. Construct a two-case comparison: one case follows the stated ideal assumptions; the other changes the most credible violated assumption. Sketch the expected shape of the dependent variable versus the relevant input. If the shape is qualitatively different, propose an experiment that will tell the models apart. Document what new evidence would make you reverse the original recommendation.

Independent problems and answer guidance

Q1. For process safety and hazard review, explain the effect of independent safeguards on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate independent safeguards as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For process safety and hazard review, explain the effect of operating limits on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate operating limits as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For process safety and hazard review, explain the effect of hazard-identification records on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate hazard-identification records as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For process safety and hazard review, explain the effect of change control on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate change control as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Learning evidence

Submit a one-page assumptions register, unit-consistent calculation, sensitivity plot and model rejection test. The quality criterion is not a single memorized numerical answer: it is the ability to explain why the conclusion may change under an independently observable perturbation.

Unit 8, Lesson 4 — Economic comparison and verification

Scope: Reactor decisions. Prerequisites: chemical material balances, algebra, introductory calculus and unit conversions.

Physical model and meaning

Engineering economics should compare consistent functional outputs and explicit uncertainty rather than ranking reactors by vessel cost alone. In the present topic, economic comparison and verification is an engineering interpretation problem before it is an algebra problem. Write the species, the observable state, the system boundary, and the governing time or length scale. The underlying quantity should be measured or inferred on an explicit basis. A change in what is held constant—reactor volume, inlet molar flow, heat capacity or concentration basis—can change the correct equation even when the equipment looks similar.

Use the working relationship Annualized cost = capital recovery + utilities + feed losses + maintenance. First identify the independent variable and the dependent response. State whether it represents a local rate, an integrated outcome, or a steady-state closure condition. Perform a dimensional check before substituting numbers. For a measured plant variable, distinguish an instrument reading from a property calculated by a constitutive expression. In particular, a model may be exact inside an idealization yet wrong when its boundary conditions fail.

Process context and a falsifiable claim

Present two reactor alternatives with different conversion and heat duty; normalize costs per usable product and run a sensitivity study. A falsifiable statement predicts how the measured response changes under a controlled, safe perturbation. Select one operating variable and hold the competing causes approximately constant. Write down what should happen if the equation is valid, what opposite observation would reject the assumption, and what additional sensor or sampling measurement can separate the explanations. In kinetic work, a mismatch between prediction and measurement may reflect a bad rate law, nonuniform temperature, nonideal flow, unrecognized side reaction, or a sampling artefact. Check those mechanisms independently rather than adjusting one fitted parameter until the chart looks plausible.

The practical question is not simply whether a numerical answer can be calculated. It is whether the proposed intervention changes the relevant physical mechanism and whether the predicted magnitude is large relative to instrument uncertainty and normal process variation. A temperature variation, for example, may alter both kinetics and properties. A flow variation may change residence time, heat removal, and mixing simultaneously. A careful experiment identifies these confounders.

Worked engineering example

Given. Alternative A annualized capital=120,000 currency units, utilities=70,000, and production=50,000 kg y⁻¹; B has 150,000, 40,000 and 60,000.

Solution. A cost proxy A=(120000+70000)/50000=3.80 per kg and B=(150000+40000)/60000≈3.17 per kg. Both exclude feedstock, separations and downtime; therefore this cannot establish full unit economics.

Interpretation. This numerical example is valid only within its stated assumptions and unit system. Carry at least two plausible significant digits through intermediate steps, then round to the resolution of the input evidence. If experimental observations differ, report the difference before modifying the calculation. Check that all flows are on comparable molar bases and that any time units in a rate constant are consistent with the residence or observation time used.

Four checks before accepting the result

1. Capital recovery basis. Identify exactly which measurement or modeling choice controls this feature. Explain its physical role, record the correct units, and consider whether an independent observation could confirm it. A single agreement at one operating point does not demonstrate that the feature is represented correctly across the entire range.

2. Uncertain utility rates. Ask what would change if this effect were twice as important as assumed. A meaningful sensitivity calculation changes one parameter while keeping the process basis fixed, then reports both the numerical response and any violated constraint. Include the consequence for experiment design.

3. Effective annual output. Separate information actually measured from information embedded in a model. A fitted coefficient may compensate for an omitted physical mechanism. Check whether different candidate models predict measurably different responses over the available operating range.

4. Cost sensitivity. Revisit the design decision after accounting for uncertainty. Where a limit is approached, an apparently precise result must not justify operating beyond validated data. Keep a record of the parameter set and the approximation used to reach each conclusion.

Limits and engineering judgement

A low nominal equipment cost may be dominated by separation, downtime, relief systems and quality variability. When this limitation is significant, do not conceal it in a footnote: label the specific prediction as a screening estimate and state what would be required for a validated design. A reactor choice depends on chemistry, phase behavior, heat exchange, material compatibility, sensor reliability and operator response. No single idealized expression can substitute for thermal-hazard testing, validated kinetic data or codes applicable to the actual facility.

Extension for the reader. Construct a two-case comparison: one case follows the stated ideal assumptions; the other changes the most credible violated assumption. Sketch the expected shape of the dependent variable versus the relevant input. If the shape is qualitatively different, propose an experiment that will tell the models apart. Document what new evidence would make you reverse the original recommendation.

Independent problems and answer guidance

Q1. For economic comparison and verification, explain the effect of capital recovery basis on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate capital recovery basis as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For economic comparison and verification, explain the effect of uncertain utility rates on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate uncertain utility rates as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For economic comparison and verification, explain the effect of effective annual output on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate effective annual output as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For economic comparison and verification, explain the effect of cost sensitivity on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate cost sensitivity as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Learning evidence

Submit a one-page assumptions register, unit-consistent calculation, sensitivity plot and model rejection test. The quality criterion is not a single memorized numerical answer: it is the ability to explain why the conclusion may change under an independently observable perturbation.

Unit 8 project — Integrated reactor technology selection

Decision brief

Compare reactor options with quantified throughput, usable yield, energy, availability, and capital recovery. Provide a table of uncertainty ranges, process hazards, vendor questions, scale-up tests and a falsification plan for the recommended architecture.

Inputs and provenance

Use clearly labeled synthetic data or independently verified measurements. For kinetic data, provide temperature and concentration units and a method for estimating variability. For operating studies, identify volumetric flow reference states and a clearly bounded control volume. Each property value needs its source, temperature and composition. Do not use reaction safety screening as a substitute for a required hazard study.

Deliverables

1. A process flow schematic with each boundary, measured variable and manipulated control.
2. A transparent mathematical model: species balances, an energy balance where relevant, parameters and numerical solver tolerances.
3. At least two physically feasible alternatives evaluated on identical input and output bases.
4. Four numerical checks: conservation, dimensional consistency, a limiting case, and sensitivity to the most uncertain parameter.
5. A testable prediction about operation under a realistic perturbation, with a criterion that would reject the model.
6. A limitations register identifying missing kinetics, transport, physical-property and safeguards evidence.

Review questions

Explain why the selected architecture is robust to its dominant uncertainty. Identify an apparently attractive but misleading performance metric and calculate the consequence of improving it at the expense of another. Explain how a tracer experiment, thermal test, feed assay or controlled startup could resolve a key uncertainty. Finally, state why the desk-study result must not authorize equipment operation or process-safety decisions.

Suggested evaluation rubric

Mass/energy accounting 25%; model assumptions and evidence 20%; correct calculations 20%; exploration of alternatives 15%; uncertainty and limitations 15%; readable reports and reproducibility 5%.

Unit 8 — Assessment: Reactor decisions

All 16 questions require an explanation of the physical premise, not only a final number. Provide units and state where a calculation is valid.

Lesson 1 — Productivity and space-time yield

Q1. For productivity and space-time yield, explain the effect of net product output on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate net product output as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For productivity and space-time yield, explain the effect of on-stream factor on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate on-stream factor as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For productivity and space-time yield, explain the effect of separation recovery on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate separation recovery as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For productivity and space-time yield, explain the effect of quality yield on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate quality yield as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Lesson 2 — Scale-up and transport limits

Q1. For scale-up and transport limits, explain the effect of surface-to-volume scaling on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate surface-to-volume scaling as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For scale-up and transport limits, explain the effect of mixing energy on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate mixing energy as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For scale-up and transport limits, explain the effect of transport/kinetics separation on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate transport/kinetics separation as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For scale-up and transport limits, explain the effect of dimensionless groups on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate dimensionless groups as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Lesson 3 — Process safety and hazard review

Q1. For process safety and hazard review, explain the effect of independent safeguards on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate independent safeguards as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For process safety and hazard review, explain the effect of operating limits on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate operating limits as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For process safety and hazard review, explain the effect of hazard-identification records on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate hazard-identification records as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For process safety and hazard review, explain the effect of change control on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate change control as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.

Lesson 4 — Economic comparison and verification

Q1. For economic comparison and verification, explain the effect of capital recovery basis on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate capital recovery basis as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q2. For economic comparison and verification, explain the effect of uncertain utility rates on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate uncertain utility rates as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q3. For economic comparison and verification, explain the effect of effective annual output on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate effective annual output as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof. Q4. For economic comparison and verification, explain the effect of cost sensitivity on the interpretation or validity of the calculated result. State an observable diagnostic, a limiting assumption and one safe engineering response.

Answer guide. Begin from the governing model and its units; isolate cost sensitivity as an explicit hypothesis. Compare a controlled model prediction against a measured trend. Reject the hypothesis if the discrepancy is systematic and larger than credible uncertainty; otherwise collect additional evidence rather than treating a point estimate as proof.