object detection in computer vision

Objectives

Enhancing efficiency of Potline Operations

object detection in computer vision

Industry Served

Aluminum

object detection in computer vision

Solution

Ripik Vision

What Calls for a Change

One of the top global producers of Aluminum products, this corporation caters to customers in nearly 50 countries. The company leverages innovative technologies to produce high-quality products and alloys, serving critical sectors such as aerospace, aviation, defence, transportation, and packaging, among others.

In the complex and competitive outlook, the firm sought reliable and high-performance recommender systems for key variables, such as AlF3. Recognizing the intricacies of the process, this pursuit aimed to enhance decision-making precision and optimize critical variables, contributing to overall operational efficiency and competitiveness in the industry.

Problem

The current software solutions fall short in meeting Key Result Area (KRA) targets, compelling the need for an enhanced approach. Particularly, in situations where operators must exercise judgment regarding AlF3 feed values, existing systems prove inadequate. With only 40% of potline baths currently operate at an optimal temperature, and a concerning 70% of the time witnesses’ instances of over-steer or under-steer. Given that power consumption constitutes over 80% of the associated costs, it becomes imperative to address the crucial factor of maintaining optimal bath temperature.

Additionally, the need for time-consuming Root Cause Analysis (RCA) further compounds the complexity of addressing and rectifying issues in the manufacturing process. To address these critical issues, there is a clear demand for reliable and high-performance recommender systems specifically tailored for key variables, such as AlF3. The software should alleviate the dependence on human judgment, mitigate deviations, and streamline the analysis of root causes for a more resource-efficient manufacturing process.

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The Solution

Ripik Vision, is one of our industrial Vision AI solutions, that proved as a transformational move in the Aluminum facility. The technology sets the stage for an illustrative potline optimization solution, featuring an AI-powered data dashboard that empowers shop floor managers with real-time tracking, analysis, and optimization capabilities across the entire plant. At the heart of our technological prowess lies patented algorithms, strategically designed to optimize the dosage of critical raw materials such as alumina and catalysts like AlF3. The suite wields substantial influence on key business outcomes in reducing energy consumption in potlines, specifically impacting current efficiency and specific power consumption. The technology provides a comprehensive solution for various tasks such as mould moisture detection, preheating time optimization, coal sizing, product inspection, finished goods counting, and tracking.

The Impact

The SaaS deployment has led to notable improvements in optimization of AlF3 dosage. Our observations indicate a substantial 20% reduction in AlF3 dosage, accompanied by improvements in specific power consumption and current efficiency. This reduction in AlF3 consumption and a 2% fuel rate reduction has led to improved energy efficiency and reduced emissions in the production of Aluminum. Through meticulous control of parameters, the technology elevates process efficiency and serves as a cornerstone in shaping crucial performance metrics. By delivering real-time recommendations for AlF3 dosage optimisation, this innovative technology adeptly oversees and sustains the potline bath temperature. This strategic intervention not only diminishes the frequency of hot pots thus enhancing operational efficiency.

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“Our collaboration with Ripik.Al for harnessing the power of Al and machine learning enabled us to optimize raw material consumption and step-up operational efficiency at our Aluminum smelters.”

Senior Vice President

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