Pulp, Paper, and Black Liquor


Assembly Line

Researchers Consider the Circular Economy in Pulp and Paper Industries

📅 Date:

✍️ Author: Reginald Davey

🔖 Topics: Sustainability

🏭 Vertical: Pulp and Paper

🏢 Organizations: University of Aveiro, University of Coimbra

The paper and pulp industries can benefit greatly from a cyclical model of manufacture. These industries are responsible for the consumption of most of the lignocellulosic biomass produced in the world. In 2020, the European paper and pulp industries alone consumed an estimated 146.5 million cubic meters of wood. The transition to a green economy approach is a chief concern in these industries.

Read more at AZO Materials

Tree to Box: The Billion Dollar Cardboard Business

Application of deep learning methods for more efficient water demand forecasting

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✍️ Author: Anjana G. Rajakumar

🔖 Topics: Convolutional Neural Network

🏢 Organizations: Hitachi

In recent years, such predictions have also found wide application in near-optimal control operations of water networks. Water demand prediction is an active field, where different methods and techniques have been applied including conventional statistical methods and machine learning methods. Due to advancements in the field of sensing and IoT, an increasing amount of data is becoming available for water distribution systems, including water demand data. Therefore, we are seeing greater use of deep learning methods to develop models for water demand forecasting in recent years as deep learning methods can deal with seasonality as well as random patterns in the data, and provide accurate results compared to traditional methods.

We observed that the frequency of data, amount of data, and quality of data has an impact on the deep learning model accuracy. In CNN-LSTM, CNN effectively extracts the inherent characteristics of historical water consumption data such as seasonality, and LSTM can fully reflect the long-term historical process and future trend. Hence, water demand forecast predictions using CNN-LSTM produced a better result when compared to other single models such as GRU, MLP, CNN and LSTM.

Read more at Industrial AI Blog

The Race To Zero Defects In Auto ICs

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✍️ Author: Anne Meixner

🏭 Vertical: Automotive, Semiconductor

While semiconductor test engineers are making great strides on isolating fab-generated defects, assembly engineers are quietly focusing attention on improving inspection and processing of equipment data to catch latent defects. This is a big deal for automotive electronics. According to a BMW presentation at the 2017 Automotive Electronics Council reliability workshop, most semiconductor devices fail within the car’s warranty period.

The carmaker noted that 22% of warranty costs are due to electronics and electrical control units. Of those failed parts, BMW said 77% of the failures are semiconductor devices, and 23% of the parts are isolated to active and passive components. Of those semiconductor failures, 48% were due to systematic fails, 24% to test coverage, 15% to random failures, and 6% were retested and did not fail the second time. The failure pareto was also broken down to 41% final test, 24% front-end processing, 22% design, and 12% assembly.

For assembly facilities to deliver 10 dppb quality to their automotive customers, they need to learn from customer returns. This requires investment in assembly equipment data collection and traceability. Latent defects that become activated during the warranty period yet pass electrical test necessitates 100% inspection to screen for these failures. Yet all this investment in more inspection and data collection places a financial strain on traditionally inexpensive assembly operations. There is constructive tension between assembly facilities and their automotive customers, as they are both cost-sensitive. Still, somehow this pathway to 10 dppb will be funded.

Read more at Semiconductor Engineering

Assembly of the Giga Press 9000t in Idra Italy | part II

Andrew Ng’s Landing AI aims to help manufacturers deploy AI vision systems

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✍️ Author: Jean Thilmany

🔖 Topics: Computer Vision, PLC

🏢 Organizations: Landing AI

Today, the company announced its LandingEdge, which customers can use to deploy deep-learning based vision inspection to their production floor. The company’s first product, Landing Lens, enables teams, who don’t have to be trained software engineers, to develop deep learning models. LandingEdge extends that capability into deployment, Yang says. “Strategically, manufacturers start AI with inspection,” Yang said. “They use cameras to repurpose the human looking at the product, which makes inspection more precise.

LandingEdge attempts to simplify the platform deployment for a manufacturer. Typically users set up a method to “train” their vision system by plugging the LandingEdge app into programmable logical controller and cameras. The PLC continuously monitors the state of cameras and the vision system itself.

Read more at VentureBeat

Siemens buys UK industrial IoT firm Senseye for global smart factory push

📅 Date:

✍️ Author: James Blackman

🏢 Organizations: Siemens, Senseye

Siemens has acquired UK-based industrial IoT firm Senseye for an undisclosed fee. Senseye, founded in 2014, provides analytics-based (“AI-powered”) predictive maintenance solutions for industrial machines, offering ways to manage and reduce unplanned downtime and to boost productivity and sustainability. The firm, headquartered in Southampton, was picked up by Zurich-based venture firm Momenta Partners as an early portfolio company; it claims its IoT sensing and analytics product, available on subscription (as-a-service), reduces unplanned machine downtime by up to 50 percent and increases maintenance staff productivity by up to 30 percent.

Read more at Enterprise IoT Insights

Surge Demand

Manufacturers band together on industrial cybersecurity. Supply chain concerns begin to dissipate in aerospace materials production while new ones emerge in agriculture fertilizer.