Multivariate Sensitivity Study
to know your process

What is Multivariate Sensitivity Study?

Multivariate Sensitivity Study allows the simultaneous examination of multiple input and output relationships and their effect on simulation results. It focuses on sensitive parameters to avoid critical operation areas of your process, to optimize the process with different constraints and objectives, and make better process-related decisions. 

For a fast calculation, the Multivariate Sensitivity Study is calculated on a cluster of CHEMCAD / DWSIM instances.

It is for every engineer who wants to visualize their process behavior, get valuable insights for optimized operation, and improve energy and production efficiencies.

Why use the Multivariate Sensitivity Study?

Cover large solution spaces

You analyze the entire solution space with full factorial or space-filling designs

Apply constraints with filters

You discover parameter dependencies and dimension reductions using big data analytics

Find pareto-optimal solutions

You visualize solution spaces and pareto fronts for multi-objective optimization problems

Discover MSS on our simulation platform

Test the data explorer presented in the video below (Use Case 2)

How sensitive is your process to
parameter changes?

Learn more from our use cases

Use Case 1

Optimization of a Batch Distillation for Solvent Recovery

Objectives and Constraints

Set-Up

Results

Challenges

  • Minimize energy demand
  • Minimize operation time
  • Maximize product yield
  • Product concentration as a constraint
  • Five design variables
  • Equidistant grid (per variable)
  • 78.125 flowsheet calculated (mass transfer batch column simulation)

Increased the solvent recovery efficiency

  • 74% energy saving
  • 4.5 h reduction in operation time
  • 7% increase in product yield
  • Infeasible design specifications cause convergence problems
  • Due to product accumulation, product specifications must be given indirectly as design specifications 
  • The correlation of design and state variables are not obvious

Use Case 2

Watch the explanatory video above

Distillation of an Ethanol-Water Mixture

Objectives and Constraints

Set-Up

Results

  • Maximize product yield
  • Minimize energy demand
  • Product concentration as a constraint
 
  • Two design variables
  • Equidistant grid (per variable)
  • 9.550 flowsheet calculated (SCDS)

Understand the correlations between variables

Flowsheet (CHEMCAD)

Flowsheet (DWSIM)

Interactive Data Explorer

Use the sliders on the report below to apply limitations! Observe the relationship between variables.

Check the sensitivities by varying:
  • EtOH fraction
  • Distillate rate
  • Reboiler duty

Know more about your process,
make better decisions