Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Wage Fairness, Growth and the Utilization of R&D Workers

In 1999, only one of three US scientists and engineers was employed to do R&D and, in several countries over the last forty to fifty years, employment of skilled workers for R&D purposes appears not to have kept pace with the overall increase in the supply of skilled workers. Low utilization of R&D personnel implies low growth per human capital endowments. To analyze the low R&D utilization/low growth equilibria, we set up an endogenous growth model in which firms set fair wages and which allows for an analysis of changes in the utilization rate of R&D workers. We find that the rise in under utilization and the fall in growth per human capital to be consistent with the increase in the demand for higher education. This could be interpreted as the “consumption” element in higher education has received an increased importance yielding a low growth effect of higher education. The results also point at problems of correctly measuring actual human capital inputs in firms.Efficiency wages; fairness; growth

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

Modelling of an axial flow compact separator using neural network

A novel design axial flow cyclonic separator called I-SEP was tested with an extensive set of experiments using air-water two phase flow mixture at atmospheric pressure. These experiments provided valuable data on the separation efficiency and pressure drop under different inlet conditions. The performance parameters i.e. Gas Carry Under (GCU) and Liquid Carry Over (LCO) were found to be non-linearly related to the inlet operating conditions. However it was found that resistance on the tangential outlet of the I-SEP affects the GCU and that manipulating the pressure difference between the two outlets and the inlet of the I-SEP through manual control valves, the GCU could be controlled. The separator was also extensively tested and compared with a gravity separator, when they were placed at the exit of a riser, in severe slugging condition frequently encountered in the production pipe work from some oil fields. The tests revealed that the I-SEP has better tendency to suppress severe slugging as compared to the gravity separator. A framework for neural network based on multiple types of input was also developed to model the separation performance of the I-SEP. Mutual Information (one of the key elements of the information theory) was applied to select the appropriate candidate input variables to the neural network framework. This framework was then used to develop a neural network model based on dimensionless input parameters such as pressure coefficient. This neural network model produced satisfactory prediction on unseen experimental data. The inverse function of a trained neural network was combined with a PID controller in a closed loop to control the GCU and LCO at a given set point by predicting the manipulating variable i.e. pressure at the I-SEP outlets. This control scheme was simulated using the test data. Such controller could be used to assist the operator in maintaining and controlling the GCU or LCO at the I-SEP outlets.The work performed during this study also includes the development of a data repository system to store and query the experimental result. An internet based framework is also developed that allows remote access of the experimental data using internet or wireless mobile devices

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Real-time predictive maintenance for wind turbines using Big Data frameworks

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs