Soft sensors

2011 m. vasario 1 d., antradienis

Benefits of using soft sensors

Product overview
Soft sensors serve the same function as physical sensors, except that values for the variable are not measured directly, but are obtained from a model of the physical sensor using other known variables as inputs.

Soft sensors are typically used to replace sensors with high maintenance requirements, short life-spans, inaccurate sensors, and for variables where evaluation involves manual sampling and laboratory analyses. Since the physically measured value is often only returned from the lab a few days later, the immediately available predicted value from the soft sensor model can be used for control purposes.

Implementing a Soft Sensor solution:

Implementing a soft sensor solution requires a structured approach based on a sound understanding of the process as a whole, and specifically, variable measurements. The procedure includes:

Data collection
Data analysis and modelling
Soft sensor model design
Performance monitoring
Deploying the process model online as a soft sensor allows for continuous feedback for system monitoring and control. This is then used in conjunction with other Smart Process Monitoring applications for further process analysis and improvement.

Use soft sensors to:Save time and money
Enable better sampling
Develop a model to predict values that are not easily or quickly measured
Replace expensive sensors with high maintenance requirements and short life-spans
Replace inaccurate sensors providing noisy, drifting readings
For proactive, consistent and increased availability of measurements
Read data to a database for offline and online processing
Use modeled outputs to develop and implement online process monitoring and control strategies

2011 m. sausio 29 d., šeštadienis

M2M solutions for data collection

The wireless machine-to-machine (M2M) world is also evolving rapidly with a growing number of devices connected to each other in various types of industrial and domestic networks. Using scalable wireless mesh networking, wireless products should be able to handle configurations scalable up to any size.

M2M wireless (including industrial applications) is sometimes regarded as the “third wave” of wireless, after office-based (Wi-Fi) and consumer based (mobile) communications.

When it comes to M2M, practitioners should make sure that new modules of any vendor offerings offer backward compatible with existing hardware and software, allowing them to leverage their existing deployments.

An interesting trend to watch in the world of industrial wireless is the increasing collaboration between traditional IT departments in the corporate office and the process engineers on the plant floors, thanks to the penetration of Ethernet in both; this spills over into areas like network monitoring, performance analysis and optimization.

In sum, wireless is continuing to grow in the industrial space as some of the newer technologies are more capable and secure. Choice does not always have to be accompanied by complexity in this case, especially when future-proofing is concerned.
See below our last project design smart system for data collection:

2011 m. sausio 27 d., ketvirtadienis

Application of soft sensors

Riveja's activities aim at developing methods and tools that optimise industrial processes. This includes enhancing the energy performance of existing equipment, such as dryers, or the optimization of energy and water consumption over an entire industrial site. R&D activities that are ongoing or in development include:

Developing analysis tools to optimize steam production and utilization in complex industrial sites
Developing methods to simultaneously optimize water and energy networks in industrial sites
Applying data mining techniques to improve wood processing performance and save energy in sawmills and other manufacturing sectors
Performing scoping study on the application of soft-sensors to improve process operation and energy performance

Our experience represent the following industrial sectors: Furniture, Pulp and Paper, Petrochemical, Oil Refining, Chemicals, and Food and Drink.

Soft Sensors to improve pulp and paper process operation and energy performance

The pulp and paper manufacturing industry usually faces a lack of real time measurement of product and process variables. This on-line unavailability of critical process variables can lead to undesirable variability, out of specifications production and higher energy consumption. A soft sensor provides on-line, accurate estimates of these variables, eliminating additional energy and production cost associated with out of specifications production. Soft sensors can also play a significant role in more complex systems used for process optimization, such as fault and diagnosis systems, and control systems.
A soft sensor is the correlation from various raw data sources to create a new source of useful information. It is an empirical model that infers process state and product quality variables that are difficult to measure on-line (composition, melt index, molecular distribution, etc.) from readily available process measurements (temperature, pressure, flow, etc.).
Riveja performed a scoping study for a possible longer-term research and development program (R&D) that would ultimately aim at increasing the adoption of soft sensors or "virtual analyzer" for process and energy performance in the pulp and paper industry.
This scoping study consists of the identification of the processes in the pulp and paper, which are the most promising candidates to the development and implementation of soft sensors. Also, the scoping study will help define a roadmap toward long term R&D program in this area with information from process experts and operators, R&D experts and sensors and control systems developers and providers.

2011 m. sausio 20 d., ketvirtadienis

Instalation for CO2 flow measurement

There is instalation at one brewery factory that I did. The system monitor and calculate flow consumtion of CO2. As hardware was used LogMessage device and ProfiSignal software.