YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ İLE ARITMA TESİSLERİNDE ENERJİ VERİMLİLİĞİ VE SÜREÇ İYİLEŞTİRME POTANSİYELİNİN DEĞERLENDİRİLMESİ

Abstract

Bu çalışma, su arıtma tesislerinde yapay zekâ (YZ) ve makine öğrenmesi (MÖ) modellerinin süreç verimliliği ve enerji tüketimi üzerindeki potansiyel kazanımlarını incelemektedir. Literatür taramasıyla SVR, ANN, CNN, PLS-R, RF/GBT, Isolation Forest ve derin pekiştirmeli öğrenme gibi modellerin küresel ölçekte havalandırma enerjisini %25-35, toplam tesis enerjisini ise %10-25 oranında düşürdüğü saptanmıştır. Türkiye özelinde atıksu arıtma sektörünün yıllık elektrik yükü yaklaşık 1,6 TWh olarak hesaplanmış, YZ tabanlı optimizasyonun %15-25 tasarruf sağlayarak yılda 0,9-1,5 milyar TL maliyet ve 110 000-180 000 t CO₂ azaltımı potansiyeli sunduğu belirlenmiştir. Model başarısının yüksek kaliteli veri akışı, proses-uyumlu algoritma seçimi ve operatör eğitimiyle doğrudan ilişkili olduğu görülmüştür. Bulgular, ulusal teşvikler ve standartlaştırılmış veri altyapısı ile desteklenen YZ/MÖ entegrasyonunun Türkiye’de sürdürülebilir su yönetimi hedeflerini önemli ölçüde ilerletebileceğini göstermektedir.

Keywords

• Yapay zekâ
• Makine öğrenmesi
• Atıksu arıtma
• Enerji verimliliği
• Süreç optimizasyonu
• Sürdürülebilirlik

ASSESSMENT OF ENERGY-EFFICIENCY AND PROCESS-IMPROVEMENT POTENTIAL IN TREATMENT PLANTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

This study evaluates the potential gains of artificial intelligence (AI) and machine-learning (ML) models for improving process efficiency and reducing energy consumption in wastewater treatment plants. A comprehensive literature review shows that models such as SVR, ANN, CNN, PLS-R, RF/GBT, Isolation Forest and deep reinforcement learning can reduce aeration energy by 25–35 % and overall plant energy use by 10–25 % worldwide. For Türkiye, the sector’s annual electrical load is estimated at roughly 1.6 TWh; AI-driven optimisation could save 15–25 % of this demand, translating into 0.9–1.5 billion TRY in operating costs and 110 000–180 000 t CO₂ emissions each year. Model success is found to depend strongly on high-quality data streams, process-specific algorithm selection and operator training. The findings indicate that national incentives and standardised data infrastructures would enable large-scale AI/ML deployment, thereby advancing Türkiye’s sustainable water-management objectives through lower energy use, improved process stability and reduced environmental impact.

Keywords

• Artificial intelligence
• Machine learning
• Wastewater treatment
• Energy efficiency
• Process optimization
• Sustainability

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