Machine Learning-Driven Multi-Objective Optimization for Intelligent Control in Forage Feed Processing

Abstract

Intelligent control systems for forage feed processing rely on real-time datasets that include parameters such as forage moisture content, particle size distribution, fiber density, and operating temperature. These data are collected continuously through embedded sensors and fed into machine learning models including Support Vector Machines (SVM), Random Forests, and Decision Trees, which are trained to optimize key process variables. Compared to traditional mechanized systems, the intelligent system achieved a 15% reduction in energy consumption, a 20% improvement in processing throughput, and a 12% increase in product quality consistency, as measured by metrics like uniform particle size and crude protein retention. Furthermore, predictive maintenance enabled by these models reduced the equipment failure rate from 6 to 1 incident per month, significantly lowering downtime and maintenance costs. Environmental impact scores also improved by 25%, due to more efficient energy use and reduced emissions. These results demonstrate the effectiveness of machine learning-driven multi-objective optimization in transforming forage feed processing into a more efficient, sustainable, and intelligent production paradigm.