Artificial Intelligence for Optimal Chemical Dosing in Water Treatment Plants: A Comparative Study Using Random Forest and XGBoost Algorithms

Ali S. Abdulhussein

doi:10.52716/jprs.v16i3.1153

المؤلفون

Ali S. Abdulhussein وزارة النفط، شركة نفط البصرة

DOI:

https://doi.org/10.52716/jprs.v16i3.1153

الكلمات المفتاحية:

Water treatment, Chemical dosing, Random Forest, XGBoost, Machine learning, Artificial intelligence, Ensemble learning, Jar test replacement.

الملخص

يقوم هذا البحث على اعتماد الذكاء الاصطناعي بدلا من الاختبارات المعملية (اختبار الجرة) في تحديد الجرعة المثلى للمواد الكيميائية (الشب، البولمر والرمل الناعم) والمستخدم لمعالجة المياه في موقع كرمة علي في محافظة البصرة والمستخدم لأغراض الحقن المكمني لحقل الرميلة النفطي وذلك باستخدام خوارزميتي XGBoost و Random Forest حيث اثبتت النتائج التوافق الكبير مع النتائج العملية والتي تم جمعها على مدار ثلاث سنوات. عند المقارنة بين هاتين الخوارزميتين اثبتت خوارزمية Random Forest تفوقها المستمر على XGBoost في الدقة ولجميع الموصفات المتغيرة لماء النهر. تدعم النتائج اعتماد الذكاء الاصطناعي لتحديد الجرعة المثلى في كل لحظة وفي جميع المواسم وبالتالي ستتحسن الطاقة التصديرية ومواصفات الماء المنتج.

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