Performance prediction of suspension freeze crystallization for the treatment of liquid hazardous wastes via machine learning methods

Wei Yuan, Wenjie Lv*, Hualin Wang, Shouzhuang Li, Hongpeng Ma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)


The experimental method for determining treatment efficiency of suspension freeze crystallization (SFC) on liquid hazardous wastes (LHWs) is accurate but complex, costly and time-consuming. In the present study, artificial neural works (ANN) and random forest (RF), two machine learning methods, were utilized to develop models that were capable of predicting the treatment performance of SFC based on 8 typical LHWs. The targeted solutes in the chosen LHWs were characterized by COD, TOC, TDS, sulfide, conductivity, etc. The models were induced and tested to predict solute removal efficiency in accordance with freezing conditions and solution characteristics of 328 pieces of data collected from previous publications. Although both models have comparable predictive power, RF model presented better prediction accuracy and power (R2 = 0.9811, RMSE = 0.0323) than ANN model (R2 = 0.9615, RMSE = 0.0481). At the same time, the RF models showed better generalization ability than ANN models regardless different LHWs. The variable importance measurement indicated that ice phase fraction was the most important factor for solute removal efficiency in SFC process. The accurate predictability of developed models could be used before actual experiment to predict the removal efficiency of SFC according to various independent variables, so as to significantly reduce experiment workload of searching for the optimum freezing conditions. The variable importance measurement could provide a right direction for adjust the higher treatment efficiency of SFC on LHWs in the real situation.

Original languageEnglish
Article number129629
Number of pages9
JournalJournal of Cleaner Production
Early online date9 Nov 2021
Publication statusPublished - 20 Dec 2021
MoE publication typeA1 Journal article-refereed


  • Artificial neural network
  • Liquid hazardous waste
  • Machine learning
  • Random forest
  • Suspension freeze crystallization


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