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Updated: May 26, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in

Puya Afshar¹, Martin Brown, Jan Maciejowski

¹Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK. p.afshar@ieee.org

IEEE Transactions on Neural Networks

|December 8, 2011

Summary

This summary is machine-generated.

This study introduces a robust multiobjective optimization system for energy-intensive industries, like papermaking. It effectively reduces energy consumption and enhances productivity while maintaining product quality.

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Area of Science:

Industrial Engineering
Process Optimization
Data-driven Decision Support

Background:

Energy-intensive industries face challenges in reducing consumption.
Multicriteria optimization is complex due to trade-offs and system interconnections.
Data-driven models have limitations in local validity and extrapolation risks.

Purpose of the Study:

To present a novel decision support system for robust multiobjective optimization of interconnected industrial processes.
To address challenges in multicriteria optimization, complex dynamics, and model validity.
To enable data-based optimization for energy saving in industries like papermaking.

Main Methods:

Process modeling by dividing plants into serially connected units, incorporating product quality, energy consumption, and uncertainty.
Utilizing a multiobjective gradient descent algorithm guided by user preferences.
Implementing the system via a MATLAB-based interactive tool, DataExplorer.

Main Results:

Demonstrated effectiveness in reducing steam consumption and increasing productivity in two UK paper mills.
Successfully optimized vacuum pressures in forming and press sections while maintaining product quality.
Validated the approach for robust multiobjective optimization in real-world industrial settings.

Conclusions:

The proposed decision support system offers a viable solution for energy saving in papermaking.
The method effectively handles multicriteria optimization challenges in complex industrial systems.
Data-driven optimization, when robustly implemented, can lead to significant operational improvements.