AI-driven carbon-neutral optimization system for smart manufacturing and energy grids

Abstract

An AI-driven optimization system for carbon-neutral manufacturing and energy applications, comprising: a data integration module for collecting real-time data from manufacturing plants, energy networks and environmental databases; an AI engine to predict energy demand, carbon intensity and operational requirements; an optimization module to determine optimal control measures to reduce emissions; a control interface for implementing the measures in manufacturing and energy network systems; and a carbon accounting module for tracking emissions, offsets and sustainability metrics.

Description

Technical field of the utility model

[0001] The present invention relates to the fields of artificial intelligence (AI), sustainable manufacturing, intelligent energy management systems, and carbon emission reduction. More specifically, it is an AI-driven system and method for optimizing manufacturing processes and energy distribution networks to achieve carbon neutrality through dynamic data analysis, predictive modeling, and real-time control. Background of the utility model

[0002] Smart manufacturing systems and smart energy grids have revolutionized industrial and urban operations by integrating automation, data analytics, and connectivity. However, with growing global concerns about climate change and carbon emissions, systems are needed that not only maximize operational efficiency but also minimize environmental impact.

[0003] Conventional energy management systems lack the built-in intelligence to holistically manage energy consumption across manufacturing and grid systems while dynamically responding to carbon intensity metrics. Furthermore, existing approaches often lack the integration of real-time renewable energy data, load forecasting, and carbon offset strategies.

[0004] There is an urgent need for an intelligent system that integrates real-time environmental data, AI algorithms, and control mechanisms to autonomously align manufacturing operations and energy distribution toward carbon-neutral targets. Brief summary of the utility model

[0005] The present invention discloses an AI-driven carbon neutrality optimization system that integrates smart manufacturing units and energy grids to enable carbon-neutral operations. The system utilizes machine learning algorithms, real-time environmental and operational data, and predictive control models to dynamically optimize energy consumption, production planning, and carbon offset strategies.

[0006] The system includes: • AI engine: Uses neural networks and reinforcement learning to predict energy demand, carbon intensity, and production requirements. • Data integration layer: Aggregates data from manufacturing facilities, renewable energy sources, grid sensors, and external environmental databases. • Optimization module: Performs multi-criteria optimization to minimize the carbon footprint while ensuring production efficiency and profitability. • Control interface: Sends real-time commands to production units, energy storage systems and power switches. • Carbon accounting and reporting system: Tracks emissions, offsets and compliance with sustainability targets.

[0007] The system enables proactive decision-making, carbon-smart load management and seamless integration of renewable energies, thus promoting sustainable manufacturing ecosystems. Detailed description of the utility model

[0008] The present invention describes an AI-driven Carbon Neutral Optimization System (CNOS) designed to minimize emissions and optimize energy consumption in smart manufacturing environments and distributed energy grids. The system is based on a coherent architecture that integrates multiple AI and data-driven components and enables dynamic, predictive control of energy sources, manufacturing processes, and emissions.

[0009] The central element is the data ingestion layer, which continuously collects real-time and historical data from various sources—including IoT sensors in manufacturing plants, smart meters, weather services, carbon pricing databases, grid status indicators, and corporate operational databases. This data is aggregated, preprocessed, and normalized to create a structured data foundation that provides insights into current energy consumption, production requirements, carbon intensities of energy sources, and renewable energy availability.

[0010] Based on this, the AI-powered forecasting engine uses combined machine learning models (e.g., LSTM networks and XGBoost) to analyze time-dependent forecasts of energy demand, production loads, and renewable energy generation based on weather data. Unsupervised learning helps detect anomalies and identify patterns in energy consumption to eliminate inefficiencies in operations.

[0011] The optimization module uses predictive information to develop concrete action strategies for reducing emissions. It applies methods such as Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO). Multi-criteria optimization algorithms such as NSGA-II are also used to simultaneously optimize emissions, energy costs, and efficiency. The module creates plans for process flows, energy resource utilization, storage strategies, and demand adjustment.

[0012] The control interface enables the implementation of optimization strategies in real time. It interacts with the physical and digital infrastructure of the manufacturing site or energy grid. It communicates bidirectionally with control systems (PLCs), building management systems, DER management systems, and MES. It supports automated load management, dynamic switching between energy sources, and intelligent storage charging processes.

[0013] An emissions dashboard serves as monitoring and decision support, visualizing relevant key figures such as current / historical emissions, CO2 savings, renewable energy use, and sustainability target achievement. The dashboard is aimed at sustainability managers, energy analysts, and operations managers and offers interactive graphs, scenario comparisons, and alerts when limit values are exceeded.

[0014] In practical application, CNOS can not only shift energy-intensive processes and identify inefficient devices, but also control load distribution in the energy grid in such a way that fossil sources are minimized and security of supply is ensured.

[0015] Overall, the system offers an intelligent, adaptable approach to achieving carbon neutrality in industrial and energy environments – with additional benefits in the form of improved resilience, cost optimization, and regulatory compliance.

Claims

[1] An AI-driven optimization system for carbon-neutral manufacturing and energy applications, comprising: a data integration module for collecting real-time data from manufacturing plants, energy networks and environmental databases; an AI engine to predict energy demand, carbon intensity and operational requirements; an optimization module to determine optimal control measures to reduce emissions; a control interface for implementing the measures in manufacturing and energy network systems; and a carbon accounting module for tracking emissions, offsets and sustainability metrics. [2] The system of claim 1, wherein the AI engine uses reinforcement learning to improve based on historical performance and external factors. [3] The system of claim 1, wherein the optimization module incorporates real-time carbon prices and the availability of renewable energy into its decisions. [4] The system of claim 1, further comprising a dashboard interface for visualizing key performance indicators, emissions trends, and operational diagnostics. [5] The system according to claim 1, wherein the CO2 accounting module is based on a blockchain network to ensure transparency and tamper resistance.