The cement industry is a vast energy guzzler and can massively bring down the cost of operations using AI applications for Energy Efficiency Improvement, Carbon Footprint Reduction, and Production & Supply Chain Efficiency Improvement. With AI, it can gain savings in the cost of production, raw material sourcing, plant maintenance, work on productivity improvement and reduction in Depreciation.

Cement making is highly resource-driven, with many manual operations and energy-intensive processes. AI shall strengthen the existing processes and make cement manufacturing more robust, environment-friendly and efficient. In the cement industry setup, every piece of equipment is unique. The system needs to be trained on each process individually and then combined for the best outcome in the plant.

The cement manufacturing process begins with gathering raw limestone, sand, iron ore and alumina, and through the process, it is mixed in the right proportions, heating at the extreme temperature. The star equipment is called a kiln, and most of the heating to create cement is done through it. After the material passes through the drying zone, pre-heating zone, decomposing zone, burning zone and cooling zone in sequence, clinker is generated as the product of the kiln process. This can be done in three ways.

  • Equipment Management: multiple types of equipment such as Raw Material Mill, Pre Heater & Calciner, Rotary Kiln, Cooling & Cement Milling are used to make cement which are heavy power consumers and prone to breakdowns and frequent maintenance. The furnace, fans, Coal Mills, Bag Filter, Heat Exchanger, and Air filters are complimentary equipment for the process.

These assets require focused monitoring and solid predictive analytics to run at their optimum and improved performance. IoT sensors and IoT-powered wireless networks will strengthen the existing monitoring system, and in conjunction with operational parameters, the data thus generated can feed into the AI model. As a result, the AI model can predict the machines' reliability and recommend the most optimum parameters for the equipment. 

  • Process: Data from the plants can be used to monitor and control the process and ensure it runs stably. AI platform, using this data, can bring in energy, productivity, and quality improvements over and above the existing baseline efficiency. The magic happens when science-based first principle models are combined with data-based models. Domain expertise in unison with AI capabilities is a powerful method that needs to be adopted by the industry.

Cement manufacturing processes of powdering, mixing, and heating processes have their carbon footprints, and they affect the environment. Understanding carbon emission in the cement production process helps companies consider how to modernize how they produce these construction materials. In any case, it's the intersection of data gleaned from physical production, and analytics tools, that coordinate change.

Cement is complex, made of raw materials and chemical interactions that create the final product. So, when these processes are controlled by automation, different types of supervising and predictions lead to dramatic changes in how people work to produce these materials and products. The AI can work dynamically to assist teams and provide valuable information to the senior management by evaluating the drying, mixing heating, or anything else.

  • Supply Chain: Logistics costs are a significant part of the total cement manufacturing and supply expense. Data Analytics is now being employed to improve logistic efficiencies. However, more powerful AI algorithms are being built to analyze the supply chain space from supplier to end customer. This can bring in significant improvement in stock availability & prediction, transit time prediction and reduction, and improved transparency and turnaround times.

Use case study, Bert Platform Solution Applications: In an ideal environment where AI takes charge of cement production, data collected from cement management and cement supply chain planning and inbound and outbound will reside on the cloud, a centralized cloud server (Bert Nova). The data gathered (Bert Maximus and Bert Qrious) of all the plants will be hosted and processed by giving intuitive pointers to create digital Twins (Bert Geminus) – of each unit and the factory floor. Bert Optimus Reinforcement Learning (RL) is done through the process of exploration/ exploitation on Digital Twin (Bert Geminus) in real-time, picks up control levers and executes them for end business objectives, production improvement and energy improvement.

Multi-agent self-learning platform enabled Bert Platform Solution actions on real-time, actual, granular data with fundamental physics of the machine with the reusable baseline models. It transferred learning and multi-agent hierarchical consensus driven on several layers of abstractions built in the platform and 360o fully automated, objective-driven real-time controls. Through this, the plant operator and chairman can predict 96% - 97% of the operating performance using the Bert Optimus predictive model.

The custom-created model combines forces from the AI model and First Principle's Modelling, controlling complex processes and steering them to higher operational efficiency. An AI-led system can operate over a much more comprehensive range, so process efficiency is achieved. Deep learning neural networks will help achieve a best-in-class output. The historical data is a complex matrix of inputs and timestamps from years of data gathered. The model tries to get the best relationships of the variables with each other over time. Multiple optimization algorithms control all the processes. For example, they provide the optimal settings (prescriptions) and the preferred settings for the plant to run. As a result, Bert Platform Solution can optimize yield and energy savings with efficient use of resources by reducing unplanned shutdowns and accidents.

Power and fuel are the primary cost of cement production (about 30% of the total cost). Among them, electricity and coal consume more energy and directly impact the cement plant's running. The electricity is used in many stages, from raw material crushing to clinker grinding. Applications of artificial intelligence (AI) and machine learning (ML) are driving the factory floor towards a better tomorrow with one click of a button to digitization, energy efficiency and carbon reduction. 

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