Conversion Processes: Catalytic Cracking (FCC) and Hydrocracking

[Book navigation](../index.html) · [Assessment](../assessments/unit-03.html) · [Applied project](../projects/unit-03.html)

Unit target

Follow carbon and hydrogen through catalytic and thermal conversion rather than treating product yields as free tuning knobs.

Engineering context

Conversion alters molecular structure and distribution. Reaction network competition, catalyst age, feed contaminants and hydrogen supply are coupled. A laboratory conversion rate is not a commercial product yield unless selectivity, hydrodynamics, secondary reactions and separation losses are addressed. 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

Evaluate incremental conversion against coke, off-gas, hydrogen, heat and quality penalties; use closed material balances before economic comparisons. 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. [Fluid catalytic cracking yield networks](../lessons/unit-03-lesson-01.html) — [interactive lab 07](../labs/lab-07.html)
2. [Hydrocracking and hydrogen addition](../lessons/unit-03-lesson-02.html) — [interactive lab 08](../labs/lab-08.html)
3. [Catalytic reforming and aromatic chemistry](../lessons/unit-03-lesson-03.html) — [interactive lab 09](../labs/lab-09.html)
4. [Thermal conversion, coking and residue routing](../lessons/unit-03-lesson-04.html) — [interactive lab 09](../labs/lab-09.html)

Laboratory strategy

These three distinct models illuminate different physics or accounting relationships in the unit: FCC conversion and yield, Hydrocracker hydrogen consumption, Reformer aromatics production. 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-03.html) to identify technical gaps. The [applied project](../projects/unit-03.html) then adds constraints from adjacent units so that a local process improvement can be evaluated for its plant-wide consequence.

Unit 03 · Lesson 01 — Fluid catalytic cracking yield networks

[Unit map](../units/unit-03.html) · [Open numerical laboratory](../labs/lab-07.html) · [Assessment](../assessments/unit-03.html) · [Applied project](../projects/unit-03.html)

The engineering problem

Fluid catalytic cracking uses a circulating solid catalyst to convert heavy feed fractions toward lighter hydrocarbons. Conversion and selectivity are influenced by feed quality, catalyst activity, contact conditions and secondary reactions. Product families include fuel gas, LPG olefins, gasoline-range liquids, cycle oils and coke. Coke combustion supplies process heat but also generates emissions.

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 fluid catalytic cracking yield networks indicates a specific decision lens within conversion. 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

Define conversion on a stated feed or boiling-range basis. In a teaching one-lump reaction, X = 1 − exp(−kτ), while individual product mass fractions must sum to conversion plus unconverted feed. Conversion and gasoline yield are not interchangeable metrics.

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

If 100 t/h feed gives 68% lump conversion and 58% of converted material becomes a gasoline-range lump, calculate gasoline lump, other converted products and unconverted feed.

Worked answer and interpretation

Converted feed is 100×0.68=68 t/h; the gasoline-range lump is 68×0.58=39.44 t/h. The other converted lumps together are 28.56 t/h and unconverted feed is 32 t/h. Sum 39.44+28.56+32=100 t/h. This is not a carbon balance because the lump compositions and coke carbon fractions are unspecified.

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

Perform mass and carbon balances and identify the destination of coke and off-gas. When evaluating severity, include product octane and sulfur, catalyst circulation, wet-gas compression and regenerator constraints. Use assay-derived conversion metrics consistently.

Place the result in a real flowsheet context. A conversion rate counts transformed material on its chosen basis; a finished-saleable yield additionally requires selectivity and downstream recovery. Side reactions, carbon laydown, gas handling and heat release must remain visible. If a proposed improvement violates elemental balance, the simulation is wrong regardless of how smooth its graph appears. 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 fitted first-order k does not represent diffusion, catalyst deactivation, regenerator temperature or full FCC kinetics. The model is for trend visualization only.

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, FCC conversion and yield](../labs/lab-07.html), provides a separate numerical sensitivity exercise. Its governing relationship is X=1−exp(−kτ); Ygasoline=X·0.62exp(−0.20X). 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 Stabilization and light-end handling as prior context and Hydrocracking and hydrogen addition 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 fluid catalytic cracking yield networks 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 fcc conversion and yield 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 03 · Lesson 02 — Hydrocracking and hydrogen addition

[Unit map](../units/unit-03.html) · [Open numerical laboratory](../labs/lab-08.html) · [Assessment](../assessments/unit-03.html) · [Applied project](../projects/unit-03.html)

The engineering problem

Hydrocracking combines molecular bond breaking and hydrogenation under catalytic conditions. Hydrogen availability, catalyst function, feed impurities and separation recycle shape achievable conversion. Unlike thermal conversion, hydrogen is a material reactant and an important cost and utility load. High pressures require certified mechanical and process-safety design.

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 hydrocracking and hydrogen addition indicates a specific decision lens within conversion. 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 mass-balance teaching proxy uses converted feed = F X and net hydrogen usage = αFX, with α stated in kg H₂ per tonne of converted feed. The proportional relationship is not a plant-specific consumption factor.

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

At 100 t/h feed and 70% conversion, assume an instructional factor of 20 kg H₂ per tonne converted. Calculate approximate net H₂ consumption and discuss why actual requirement needs species-level accounting.

Worked answer and interpretation

Converted mass is 100×0.70=70 t/h. With 20 kg H₂ per tonne converted, net hydrogen demand is 1400 kg/h, or 1.4 t/h. The calculation does not define an actual recycle-gas circulation flow or account separately for organic heteroatoms; the factor needs calibration to feed composition and product objectives.

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

Identify net versus circulating hydrogen, quench duty, gas purification, catalyst life and distillate product properties. Close both hydrogen supply and product yield balances before comparing hydrocracking with FCC.

Place the result in a real flowsheet context. A conversion rate counts transformed material on its chosen basis; a finished-saleable yield additionally requires selectivity and downstream recovery. Side reactions, carbon laydown, gas handling and heat release must remain visible. If a proposed improvement violates elemental balance, the simulation is wrong regardless of how smooth its graph appears. 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

Conflating hydrogen recycle flow with hydrogen consumption can overstate feedstock demand by orders of magnitude. Real consumption includes heteroatom removal and aromatic saturation.

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, Hydrocracker hydrogen consumption](../labs/lab-08.html), provides a separate numerical sensitivity exercise. Its governing relationship is X=1−exp(−kτ), H2=F X α /1000. 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 Fluid catalytic cracking yield networks as prior context and Catalytic reforming and aromatic chemistry 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 hydrocracking and hydrogen addition 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 hydrocracker hydrogen consumption 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 03 · Lesson 03 — Catalytic reforming and aromatic chemistry

[Unit map](../units/unit-03.html) · [Open numerical laboratory](../labs/lab-09.html) · [Assessment](../assessments/unit-03.html) · [Applied project](../projects/unit-03.html)

The engineering problem

Catalytic reforming increases certain gasoline-range quality attributes by rearranging and dehydrogenating hydrocarbons in a selected feed. Aromatization, isomerization and hydrocracking compete; feed pretreatment is important for catalyst protection. Hydrogen co-production can support treating units, but product yield and composition must be tracked together.

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 catalytic reforming and aromatic chemistry indicates a specific decision lens within conversion. 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

Illustrative conversion may be X = 1 − exp(−k(T)τ), with Arrhenius k(T) = kref exp[(Ea/R)(1/Tref − 1/T)]. An aromatic product lump can be represented as F X S, where selectivity S has an explicitly defined basis.

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 100 t/h notional feed, 65% conversion and aromatic-lump selectivity of 75%, calculate aromatic-lump output and explain why this is not an assay of gasoline benzene content.

Worked answer and interpretation

Under the declared selectivity definition, aromatic-lump output = 100×0.65×0.75 = 48.75 t/h. Unconverted material is 35 t/h, and 16.25 t/h is converted into other products. There is no direct inference about benzene content, which requires species-level analysis and measured reformate composition.

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

Check changes in octane quality alongside reformate volume, aromatics and benzene constraints, hydrogen yield, catalyst age and furnace duty. Each performance number must have a test and reporting convention.

Place the result in a real flowsheet context. A conversion rate counts transformed material on its chosen basis; a finished-saleable yield additionally requires selectivity and downstream recovery. Side reactions, carbon laydown, gas handling and heat release must remain visible. If a proposed improvement violates elemental balance, the simulation is wrong regardless of how smooth its graph appears. 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

Higher temperature does not imply unlimited better product: deactivation, equilibrium, side reactions and safety limits can dominate. A simple Arrhenius proxy ignores those couplings.

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, Reformer aromatics production](../labs/lab-09.html), provides a separate numerical sensitivity exercise. Its governing relationship is k=kref exp(Ea/R(1/Tref−1/T)); Y=X S. 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 Hydrocracking and hydrogen addition as prior context and Thermal conversion, coking and residue routing 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 catalytic reforming and aromatic chemistry 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 reformer aromatics production 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 03 · Lesson 04 — Thermal conversion, coking and residue routing

[Unit map](../units/unit-03.html) · [Open numerical laboratory](../labs/lab-09.html) · [Assessment](../assessments/unit-03.html) · [Applied project](../projects/unit-03.html)

The engineering problem

Delayed coking thermally converts heavy residues while creating solid coke and gas/liquid products. Visbreaking targets viscosity reduction with a smaller conversion objective. Thermal severity influences light products, coke and operational reliability, while furnace residence and fouling determine how reliably a campaign runs.

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 thermal conversion, coking and residue routing indicates a specific decision lens within conversion. 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

Use a mass balance F = coke + gas + liquids + other measured outputs. Yield fractions defined on feed must sum to unity. Thermal conversion selection should be evaluated against downstream upgrading or disposal routes for each product family.

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

From 100 t/h residue, assume illustrative yields of 22% coke, 9% gas, 58% liquid and 11% other streams. Reconcile tonnes per hour, then identify which streams need further composition analysis.

Worked answer and interpretation

For 100 t/h residue, the four synthetic outlet classes are 22, 9, 58 and 11 t/h respectively. Their sum of 100 t/h shows algebraic closure. In a real coker, water, entrained solids, coke moisture, gas chemistry and inventories need specified boundaries before a useful reconciled balance can be claimed.

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

Track coke properties and handling, off-gas sulfur and treatment, coker fractionator loading, feed metallurgy and hot-equipment limits. An apparent high liquid yield may be unattractive if its quality and downstream treatment load are poor.

Place the result in a real flowsheet context. A conversion rate counts transformed material on its chosen basis; a finished-saleable yield additionally requires selectivity and downstream recovery. Side reactions, carbon laydown, gas handling and heat release must remain visible. If a proposed improvement violates elemental balance, the simulation is wrong regardless of how smooth its graph appears. 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

Temperature alone cannot predict coke yield from a multi-component residue. Feed asphaltenes, residence time, recycle and reactor operation matter.

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, Reformer aromatics production](../labs/lab-09.html), provides a separate numerical sensitivity exercise. Its governing relationship is k=kref exp(Ea/R(1/Tref−1/T)); Y=X S. 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 Catalytic reforming and aromatic chemistry as prior context and Hydrodesulfurization and sulfur balances 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 thermal conversion, coking and residue routing 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 reformer aromatics production 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 03 applied project — Conversion

[Unit 03](../units/unit-03.html) · [Book projects](../index.html)

Commissioning question

A fictional integrated refinery-and-petrochemicals teaching facility is reviewing its conversion 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: Follow carbon and hydrogen through catalytic and thermal conversion rather than treating product yields as free tuning knobs.

Conversion alters molecular structure and distribution. Reaction network competition, catalyst age, feed contaminants and hydrogen supply are coupled. A laboratory conversion rate is not a commercial product yield unless selectivity, hydrodynamics, secondary reactions and separation losses are addressed.

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 Fluid catalytic cracking yield networks, Hydrocracking and hydrogen addition, Catalytic reforming and aromatic chemistry, Thermal conversion, coking and residue routing. 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 07: FCC conversion and yield](../labs/lab-07.html), [lab 08: Hydrocracker hydrogen consumption](../labs/lab-08.html), [lab 09: Reformer aromatics production](../labs/lab-09.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

Evaluate incremental conversion against coke, off-gas, hydrogen, heat and quality penalties; use closed material balances before economic comparisons. 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 05: Atmospheric cut sensitivity

[Open lab in the browser](lab-05.html) · [Unit 02](../units/unit-02.html)

Engineering purpose

Show how a moving middle cutpoint changes the distribution between two ideal assay ranges. 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

Ymiddle=F(Tmid)−F(200 °C), Yheavy=F(500 °C)−F(Tmid)

The result is reported as % of feed. 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

Identify the cutpoint change that shifts 5% of feed from the heavy into the middle range. 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

Sharp-cut synthetic TBP; mass conserved but no column VLE or hydraulics. 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 06: Vacuum boiling temperature

[Open lab in the browser](lab-06.html) · [Unit 02](../units/unit-02.html)

Engineering purpose

Approximate the pressure dependence of one pseudo-component boiling temperature. 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

1/T = 1/T0 − (R/ΔHvap)ln(P/P0)

The result is reported as K. 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 boiling temperature at 10 and 25 kPa; explain why vacuum does not guarantee no coking. 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

Constant enthalpy and one pseudo-component; no thermal cracking or equipment duty. 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 19: Hydrogen availability network

[Open lab in the browser](lab-19.html) · [Unit 07](../units/unit-07.html)

Engineering purpose

Compute contained hydrogen versus a notional combined network demand. 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

Havailable=F1y1+F2y2; deficit=max(0,D−Havailable)

The result is reported as kg/h deficit. 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

Determine supply threshold and distinguish contained from deliverable hydrogen. 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

Contained H₂ is not usable H₂ without pressure and purity compatibility. 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 03 assessment — Conversion

[Return to unit](../units/unit-03.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.

Fluid catalytic cracking yield networks

Question 01 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — If 100 t/h feed gives 68% lump conversion and 58% of converted material becomes a gasoline-range lump, calculate gasoline lump, other converted products and unconverted feed. — 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 fluid catalytic cracking yield networks. 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 A fitted first-order k does not represent diffusion, catalyst deactivation, regenerator temperature or full FCC kinetics. The model is for trend visualization only. 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 fluid catalytic cracking yield networks. Use this practice context: Perform mass and carbon balances and identify the destination of coke and off-gas. When evaluating severity, include product octane and sulfur, catalyst circulation, wet-gas compression and regenerator constraints. Use assay-derived conversion metrics consistently. 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.

Hydrocracking and hydrogen addition

Question 05 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — At 100 t/h feed and 70% conversion, assume an instructional factor of 20 kg H₂ per tonne converted. Calculate approximate net H₂ consumption and discuss why actual requirement needs species-level accounting. — 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 hydrocracking and hydrogen addition. 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 Conflating hydrogen recycle flow with hydrogen consumption can overstate feedstock demand by orders of magnitude. Real consumption includes heteroatom removal and aromatic saturation. 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 hydrocracking and hydrogen addition. Use this practice context: Identify net versus circulating hydrogen, quench duty, gas purification, catalyst life and distillate product properties. Close both hydrogen supply and product yield balances before comparing hydrocracking with FCC. 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.

Catalytic reforming and aromatic chemistry

Question 09 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — For 100 t/h notional feed, 65% conversion and aromatic-lump selectivity of 75%, calculate aromatic-lump output and explain why this is not an assay of gasoline benzene content. — 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 catalytic reforming and aromatic chemistry. 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 Higher temperature does not imply unlimited better product: deactivation, equilibrium, side reactions and safety limits can dominate. A simple Arrhenius proxy ignores those couplings. 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 catalytic reforming and aromatic chemistry. Use this practice context: Check changes in octane quality alongside reformate volume, aromatics and benzene constraints, hydrogen yield, catalyst age and furnace duty. Each performance number must have a test and reporting convention. 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.

Thermal conversion, coking and residue routing

Question 13 — Reconstruct the quantitative link

Given the synthetic case posed in the lesson — From 100 t/h residue, assume illustrative yields of 22% coke, 9% gas, 58% liquid and 11% other streams. Reconcile tonnes per hour, then identify which streams need further composition analysis. — 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 thermal conversion, coking and residue routing. 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 Temperature alone cannot predict coke yield from a multi-component residue. Feed asphaltenes, residence time, recycle and reactor operation matter. 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 thermal conversion, coking and residue routing. Use this practice context: Track coke properties and handling, off-gas sulfur and treatment, coker fractionator loading, feed metallurgy and hot-equipment limits. An apparent high liquid yield may be unattractive if its quality and downstream treatment load are poor. 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.