SUN'IY INTELLEKT YORDAMIDA FLOTATSIYA JARAYONLARINI OPTIMALLASHTIRISH

Abstract

Konchilik sanoatida rudalar sifatining pasayishi va ishlab chiqarish xarajatlarining ortishi boyitish jarayonlarini yanada samarali boshqarishni talab qilmoqda. Sun'iy intellekt (SI), mashinaviy o‘qitish (ML) va chuqur o‘qitish (Deep Learning) texnologiyalari flotatsiya jarayonlarini real vaqt rejimida monitoring qilish va optimallashtirish uchun istiqbolli vositalar hisoblanadi. Ushbu maqolada sun'iy intellekt asosida flotatsiya ko‘pigini tahlil qilish, reagent sarfini optimallashtirish, konsentrat sifatini prognozlash va metall ajralib chiqish darajasini oshirish imkoniyatlari ko‘rib chiqilgan. Tadqiqotlar shuni ko‘rsatadiki, neyron tarmoqlar va kompyuter ko‘rish texnologiyalari yordamida flotatsiya jarayonining samaradorligini oshirish, energiya sarfini kamaytirish va ishlab chiqarish xarajatlarini qisqartirish mumkin.

In the mining industry, the decline in ore quality and rising production costs necessitate more efficient management of enrichment processes. Artificial intelligence (AI), machine learning (ML), and deep learning (Deep Learning) technologies are promising tools for monitoring and optimizing flotation processes in real time. This article examines the possibilities of analyzing flotation foam based on artificial intelligence, optimizing reagent consumption, predicting concentrate quality, and increasing metal recovery rates. Research shows that using neural networks and computer vision technologies can increase the efficiency of the flotation process, reduce energy consumption, and reduce production costs.