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GP-CH-0IFQYR9ChemicalOpen for request

Water-treatment adsorption optimisation

A water-treatment modelling workbench for analysing adsorption experiments and optimising selected operating factors through isotherms, kinetics, and response-surface methods.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • NumPy
  • SciPy
  • Docker

Project definition

Problem statement

Adsorption performance changes with initial concentration, dose, pH, contact time, temperature, mixing, particle size, and measurement uncertainty. A high removal percentage alone can hide low capacity or an uneconomic adsorbent dose.

The engineering problem is to calculate adsorption responses correctly, compare kinetic and equilibrium models, design multivariable experiments, and validate an optimum with independent data.

Project objectives

  • Prepare batch adsorption records with mass, volume, concentration, time, conditions, blanks, and replicates.
  • Calculate removal, equilibrium capacity, uncertainty, and basic mass-balance checks.
  • Fit selected isotherm and kinetic models using nonlinear regression and residual diagnostics.
  • Create and analyse a response-surface experiment for selected bounded factors.
  • Optimise multiple responses and compare predictions with confirmation experiments.

System design

System modules

01

Experiment manager

Stores adsorbent, water matrix, target compound category, batch conditions, measurements, blanks, replicates, and units.

02

Quality and calculation module

Checks ranges, detection limits, blanks, replicates, mass balance, removal, and adsorption capacity.

03

Isotherm and kinetic fitter

Fits selected nonlinear models and reports parameters, uncertainty, residuals, and model criteria.

04

RSM designer

Creates selected central-composite or Box-Behnken designs and analyses factor and interaction effects.

05

Optimisation dashboard

Shows response surfaces, desirability or constrained tradeoffs, uncertainty, and confirmation error.

Methodology

System workflow

  1. 01
    Define adsorption study

    The student selects a prepared safe system, factors, ranges, responses, measurement method, and constraints.

  2. 02
    Create or import experiments

    A randomised design is generated or prepared measurements are validated with blanks and replicates.

  3. 03
    Fit models

    Kinetic, isotherm, and response-surface models are fitted with recorded bounds and diagnostics.

  4. 04
    Find optimum

    The system searches within tested ranges for the declared removal, capacity, time, and dose objectives.

  5. 05
    Confirm

    Predicted responses are compared with separate confirmation results and the uncertainty is reported.

Demonstration scenario

A prepared adsorption dataset varies pH, dose, contact time, and concentration. The workbench compares kinetic and isotherm models, fits a response surface for removal and capacity, identifies a bounded tradeoff, and checks it against separate confirmation measurements.

Engineering

Technical architecture

Web application
Nuxt and Vue for experiment tables, model curves, residuals, response surfaces, and confirmation results.
Modelling API
FastAPI for designs, observations, calculations, fits, optimisation, validation, and exports.
Data layer
PostgreSQL for materials, experiments, factors, measurements, models, parameters, and confirmations.
Numerical engine
Python, NumPy, and SciPy for nonlinear fitting, uncertainty, response surfaces, optimisation, and diagnostics.
Verification
Unit tests, synthetic parameter-recovery cases, mass-balance checks, and independent confirmation records.

Testing

Evaluation

Evaluation measures

  • Adsorption-capacity and removal calculation correctness
  • Parameter recovery on synthetic isotherm and kinetic datasets
  • Fit error, residual pattern, confidence intervals, and model-selection criteria
  • Response-surface prediction error under cross-validation
  • Difference between predicted optimum and confirmation results
  • Sensitivity to measurement noise, outliers, factor range, and model choice

System boundaries

  • The software uses safe prepared data or experiments conducted under approved laboratory supervision.
  • It does not provide instructions for handling toxic compounds, pathogens, or uncontrolled chemical waste.
  • Model results apply only to the tested adsorbent, water matrix, factor ranges, and measurement method.
  • Treatment design requires regeneration, disposal, hydraulics, scale-up, safety, cost, and regulatory studies beyond this project.

Included

  1. 01Adsorbent, contaminant, experiment, and measurement interface
  2. 02Isotherm, kinetic, RSM, optimisation, and validation modules
  3. 03Fit, residual, response-surface, and confirmation dashboards
  4. 04Prepared safe adsorption datasets, experiments, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, adsorption and experiment diagrams, modelling results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-CH-0IFQYR9
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.