# Optimized data exploration applied to the simulation of a chemical process

## TL;DR

This paper introduces a novel iterative algorithm for exploring complex parameter spaces in chemical process simulations, improving feasibility classification and guiding exploration towards regions of interest using machine learning techniques.

## Contribution

The paper presents a new exploration algorithm combining machine learning methods to efficiently identify feasible regions in unknown parameter spaces, with improvements over existing Kriging-based approaches.

## Key findings

- Outperforms Kriging-based exploration in binary feasibility classification
- Successfully applied to industrial chemical process simulation
- Achieves good data space approximation with few data points

## Abstract

In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.