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ICAR AICE-JRF/SRF Food Process Modelling, Simulation & Optimisation: practice questions

ICAR AICE-JRF/SRF (PhD) — Food Processing Technology, Food Safety & Quality, Processing and Food Engineering · India · ICAR through the National Testing Agency

Original practice questions written to the published syllabus · not past papers
10worked here, free
90written to this exam in this subject
11syllabus units

What this subject covers

Building and using models of food processes: kinetic models of quality and microbial change, numerical solution of heat and mass transfer, computational fluid dynamics, multi-objective optimisation, data-driven and machine-learning models, and digital twins of food plants.

In the exams: PhD research; ICAR AIEEA-PG Code 18 Unit II (system analysis, automation and online data acquisition)

Where the 90 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Food Process Modelling, Simulation & Optimisation. Difficulty: 15 easy, 64 medium, 11 hard.

Types of models: empirical, mechanistic and data-driven9
Numerical methods: finite difference and finite element9
Computational fluid dynamics in food processing9
Optimisation methods: single- and multi-objective9
Machine learning and artificial intelligence in food processing9
Digital twins and Industry 4.0 in food plants9
Process simulation software8
Uncertainty and sensitivity analysis8
Modelling heat transfer in thermal processing7
Modelling simultaneous heat and mass transfer in drying and frying7
Kinetic modelling of quality, nutrient and microbial changes6

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Types of models: empirical, mechanistic and data-driven · easy

Which type of model is derived from conservation laws and transport equations?

  1. Empirical (black-box)
  2. Mechanistic (white-box)
  3. Random-number model
  4. Purely statistical regression
Answer and working

B. Mechanistic (white-box)

Mechanistic models use physical, chemical and biological laws; empirical models fit observed data.

Further reading: Tijskens, Hertog and Nicolaï, Food Process Modelling

2 · Numerical methods: finite difference and finite element · medium

Why is a zero-gradient (Neumann) condition used at the centre of a symmetric slab?

  1. The centre is always at the medium temperature
  2. The centre has an infinite thermal conductivity
  3. No net heat crosses the central symmetry plane
  4. It makes the numerical solution unstable
Answer and working

C. No net heat crosses the central symmetry plane

By symmetry the temperature profile has a maximum or minimum at the centre, so ∂T/∂x = 0.

Further reading: Datta and Rakesh, Modeling of Transport Processes

3 · Modelling simultaneous heat and mass transfer in drying and frying · medium

A Page model MR = exp(−ktⁿ) has k = 0.01 min⁻ⁿ and n = 1.2. What is MR after 60 min?

  1. 0.74
  2. 0.01
  3. 0.55
  4. 0.26
Answer and working

D. 0.26

60^1.2 = 136.1; MR = exp(−0.01 × 136.1) = 0.26. 0.55 uses n = 1 (exp(−0.6)); 0.74 uses t^0.5; 0.01 confuses MR with k.

Further reading: Datta, Biological and Bioenvironmental Heat and Mass Transfer

4 · Modelling simultaneous heat and mass transfer in drying and frying · medium

Why should shrinkage be included in models of fruit drying?

  1. It raises the wet-bulb temperature of the drying air
  2. It shortens the diffusion path
  3. It has no effect at all on moisture transport
  4. It adds water back into the drying product
Answer and working

B. It shortens the diffusion path

Volume loss changes dimensions and properties during drying.

Further reading: Datta, Biological and Bioenvironmental Heat and Mass Transfer

5 · Computational fluid dynamics in food processing · medium

Which model represents a stacked pallet of fruit in a cold-store CFD simulation without meshing every fruit?

  1. A porous-media model
  2. A discrete droplet model
  3. A laminar pipe-flow model
  4. A volume-of-fluid model
Answer and working

A. A porous-media model

Porous media represent resistance to airflow and heat exchange in the stack.

Further reading: Sun, Computational Fluid Dynamics in Food Processing

6 · Process simulation software · easy

What does a steady-state flowsheet simulator mainly calculate?

  1. The DNA sequence of the raw materials used
  2. Sensory scores of the products made
  3. Only the cost of the labour on each shift
  4. Mass and energy balances
Answer and working

D. Mass and energy balances

Flowsheet simulators solve balances, sizing and utilities.

Further reading: Tijskens et al., Food Process Modelling

7 · Machine learning and artificial intelligence in food processing · medium

What is data leakage in ML?

  1. Sharing data with colleagues
  2. Using too few features
  3. Data lost by a computer failure
  4. Test or future information entering training
Answer and working

D. Test or future information entering training

Leakage inflates apparent performance.

Further reading: Verboven et al. (2020)

8 · Uncertainty and sensitivity analysis · medium

In first-order uncertainty propagation for independent inputs, how is output variance approximated?

  1. Product of all input means
  2. Largest input variance only
  3. Sum of input means
  4. Sum of squared sensitivities times input variances
Answer and working

D. Sum of squared sensitivities times input variances

Var(y) ≈ Σ(∂y/∂x_i)² Var(x_i).

Further reading: Saltelli et al., Global Sensitivity Analysis

9 · Digital twins and Industry 4.0 in food plants · easy

What distinguishes a digital twin from a digital model?

  1. A digital model is always the more accurate one
  2. There is no difference between the two terms
  3. A twin uses live data and informs decisions
  4. A twin has no mathematical model inside it
Answer and working

C. A twin uses live data and informs decisions

A digital model is offline; a shadow receives data; a twin also feeds decisions back.

Further reading: Verboven et al. (2020), Digital twins of food process operations

10 · Digital twins and Industry 4.0 in food plants · easy

Which is a challenge for Industry 4.0 in food plants?

  1. Lack of any machines
  2. No use for data at all
  3. Too few products to make
  4. Hygienic sensors, cybersecurity and data quality
Answer and working

D. Hygienic sensors, cybersecurity and data quality

Washdown conditions, security of connected control systems and data standards are key issues.

Further reading: Verboven et al. (2020), Digital twins of food process operations

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Read the subject

Food Process Modelling, Simulation & OptimisationThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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