Author Profile
Chief Automations Officer & OWI Platform Architect. Chief Automations Officer and architect of the Optimal Waste Intelligence (OWI) platform. Builds the machine-learning models, computer-vision sorting, industrial IoT sensor networks, and closed-loop process control that run modern waste-to-energy operations in real time. CTO of FueledBy.Net and co-founder of The Waste Agency. Published by Renewable Waste Energy.
Most industrial waste diversion numbers fail audit because nobody characterized the roll-off. A process engineer on what to measure before buying a sorter.
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The 76% C&D waste recovery rate is mostly crushed concrete. The wood and gypsum that need a robot and a buyer are where diversion quietly falls apart.
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Generative AI copilots help most in a waste plant's back office, not on the line. An engineer on where LLMs pay off, where they hallucinate, and why.
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Waste Connections, Republic and GFL just disclosed real AI waste management ROI numbers. An engineer reads the margin math and finds where it breaks.
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A waste to energy power plant makes about 550 kWh a tonne, but the turbine isn't what decides that. An engineer walks the line from bunker to export meter.
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Real-time waste analytics fails at the data layer, not the dashboard. An engineer on what the screen shows, where the numbers break, and what it costs.
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Most industrial AI projects fail, and rarely because the model is weak. After building OWI, here's what actually breaks them: data, sensors, and integration.
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TOMRA GAINnext hit 95% PET tray purity and a 51% PolyPerception stake at IFAT 2026. An engineer's read on recall, conversational plant control, and copper meatballs.
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How waste intelligence software uses sensor fusion and AI to predict feedstock composition and drive real-time process control at WTE facilities.
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How IoT sensors and AI transform waste collection operations. Real ROI data, implementation requirements, and how smart bins connect to processing plants.
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Predictive maintenance in waste processing plants uses AI and sensor data to prevent unplanned downtime, cutting repair costs 25-40% and boosting throughput.
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How computer vision and deep learning classify waste materials at belt speed, achieving 95%+ accuracy. Architecture, training data, deployment, and real-world performance.
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How modern waste sorting technology combines AI classification, robotic arms, and optical sensors to achieve 95%+ purity rates and unlock higher-value recovery.
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How artificial intelligence and machine learning optimize waste sorting, processing, and energy recovery. Real applications from classification to predictive maintenance.
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