Scholarlink

<p>This study investigates the optimization and performance modeling of a spray flash desalination system, integrating experimental design with machine learning techniques, including Random Forest (RF), Gradient Boosting (GB), and Decision Tree (DT) models. The experimental analysis varied four key parameters namely, vacuuming pressure (20–60&#xa0;kPa), salinity (20,000–40,000 ppm), feeding temperature (25–45&#xa0;°C), and flow rate (0.3–0.5&#xa0;L·min<sup>− 1</sup>) to optimize distillate production. The results showed an optimal distillate output of 7,585 mL·h⁻¹, with a specific energy consumption (SEC) of 125.4 kWh·m⁻³ and a gain output ratio (GOR) of 11.6. Machine learning models demonstrated strong prediction accuracy, with RF achieving a root mean squared error (RMSE) of 545 mL·h⁻¹ and R² of 0.93. The interpretability using SHAP analysis shows that feed temperature has a significant impact on prediction. This research presents a novel hybrid approach combining response surface methodology and machine learning to optimize desalination systems, providing a robust framework for improving energy efficiency and performance in future large-scale desalination applications.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Experimental Optimization Coupled with Advanced Machine Learning Modeling of a Spray Flash Desalination System

  • 期刊论文

  • 发表日期：

    2026年4月15日

查看全文
  • Faizan Ahmed,
  • Nayeemuddin Mohammed,
  • Hiren Mewada,
  • Feroz Shaik

摘要

This study investigates the optimization and performance modeling of a spray flash desalination system, integrating experimental design with machine learning techniques, including Random Forest (RF), Gradient Boosting (GB), and Decision Tree (DT) models. The experimental analysis varied four key parameters namely, vacuuming pressure (20–60 kPa), salinity (20,000–40,000 ppm), feeding temperature (25–45 °C), and flow rate (0.3–0.5 L·min− 1) to optimize distillate production. The results showed an optimal distillate output of 7,585 mL·h⁻¹, with a specific energy consumption (SEC) of 125.4 kWh·m⁻³ and a gain output ratio (GOR) of 11.6. Machine learning models demonstrated strong prediction accuracy, with RF achieving a root mean squared error (RMSE) of 545 mL·h⁻¹ and R² of 0.93. The interpretability using SHAP analysis shows that feed temperature has a significant impact on prediction. This research presents a novel hybrid approach combining response surface methodology and machine learning to optimize desalination systems, providing a robust framework for improving energy efficiency and performance in future large-scale desalination applications.

Graphical Abstract