Sensor Design
Assembly Line
🗜️ How to Monitor Material Levels in Tanks to Improve Supply Chain Management
Sensing and measuring the amount of solid, fluid, or granulated materials stored in tanks has become increasingly important due to supply chain challenges and the need to monitor inventory levels and control manufacturing processes closely. Depending on the application, level sensors can be required to be food-safe, withstand high pressures, temperatures, or vibrations, be used in corrosive environments with high resistance to acids and bases, and have a high degree of electrical and thermal isolation to ensure safe operation.
While it’s possible to design level sensors, it’s a complex task fraught with risk. The process begins with matching the measurement technology, such as capacitive, magnetic, ultrasonic, or optical sensing, with the application. The next step is selecting the housing, components, and other materials to support the operating environment. It’s also often necessary to gain safety and regulatory approvals and ensure that the design achieves the required ingress protection (IP) rating. Instead, designers can turn to pre-engineered level sensing solutions that ensure accurate and reliable measurements and speed time to market.
A High-speed and High-precision Color Sensor for Improving Color Management in the Paper-making Process
Thanks to the expanding retail business, the paperboard market in Asia is growing and thus the demand for paper color control is increasing. To meet this need, online measurement of paper chromaticity in the paper-making process is used to ensure strict quality control of paper color. Yokogawa has enhanced the functions of the LED color sensor for the B/M9000VP paper quality control system. A new high-sensitivity spectroscope enables high-sensitivity and high-speed measurements, and a moisture-proof coating on components has improved moisture resistance. With the enhanced functionality and robustness of the LED color sensor, the B/M9000VP has improved quality control in the paper-making process.
AI in the Process Industry
When applying AI to difficult problems in plants, approaches differ depending on whether AI researchers can access useful information derived from similar problems. This article first discusses how to search and identify useful research and literature. If well established AI research is available, the next step is simply to choose an appropriate AI platform. If not, the most serious bottleneck for the problem-solving task arises: how to integrate plant domain knowledge and AI technology. This article presents a solution to the latter case. This solution enables plant engineers to make full use of AI geared for themselves, not for data scientists. AI-based control, which is one of the promising AI applications for plants and is expected to solve difficult problems in plants, is also discussed.