Application of gaussian processes to online approximation of compressor maps for load-sharing in a compressor station

Devising optimal operating strategies for a compressor station relies on the knowledge of compressor characteristics. As the compressor characteristics change with time and use, it is necessary to provide accurate models of the characteristics that can be used in optimization of the operating strategy. This paper proposes a new algorithm for online learning of the characteristics of the compressors using Gaussian Processes. The performance of the new approximation is shown in a case study with three compressors. The case study shows that Gaussian Processes accurately capture the characteristics of compressors even if no knowledge about the characteristics is initially available. The results show that the flexible nature of Gaussian Processes allows them to adapt to the data online making them amenable for use in real-time optimization problems

Measuring Lecturers' Perception of Transition to E-Learning Systems and Digital Divide: A Case Study in School of Business Administration of Istanbul University

The development of internet technology has affected spread of e-learning programs. Students have been attending e-learning programs worldwide. For example, students in Turkey have increasingly been using this technology to attend e-MBA programs. Due to constraints like lecturers, ability to use technology and the infrastructure of information technologies in current education systems it is not possible to reach high levels of higher education. In this paper, the authors measure lecturers' behaviors toward e-learning activities. The study gauges their ability to use information technology, perceptions about the advantages of e-learning program, and readiness for the transition. Data are provided from a survey conducted in the School of Business Administration at Istanbul University. Descriptive statistics and nonparametric tests are used to analyze the data. These tests measure the digital divide between academicians taking into account their academic qualifications, gender and departments

Measuring Lecturers' Perception of Transition to E-Learning Systems and Digital Divide: A Case Study in School of Business Administration of Istanbul University

The development of internet technology has affected spread of e-learning programs. Students have been attending e-learning programs worldwide. For example, students in Turkey have increasingly been using this technology to attend e-MBA programs. Due to constraints like lecturers, ability to use technology and the infrastructure of information technologies in current education systems it is not possible to reach high levels of higher education. In this paper; the authors measure lecturers' behaviors toward e-learning activities. The study gauges their ability to use information technology, perceptions about the advantages of e-learning program, and readiness for the transition. Data are provided from a survey conducted in the School of Business Administration at Istanbul University. Descriptive statistics and nonparametric tests are used to analyze the data. These tests measure the digital divide between academicians taking into account their academic qualifications, gender and departments

Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement

As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based upon previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data is hard to measure online and it often takes time to acquire them from off-site lab analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this paper proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base adaptation

Model predictive approaches for active surge control in centrifugal compressors

\u3cp\u3eModel predictive control (MPC) techniques are considered for industrial centrifugal compression systems with nonlinear dynamics, to address process and antisurge control for reaching the desired pressure ratio and surge distance. We consider a contractive nonlinear MPC formulation that ensures asymptotic stability of the closed-loop system by imposing the decrease of a quadratic Lyapunov function via an additional constraint. We discuss recursive feasibility and estimate the region of attraction via numerical methods. We also consider alternative MPC formulations, including offset-free linear and nonlinear MPC to handle the effects of disturbances and unmodeled dynamics. The computational efficiency of an approximation based on sequential quadratic programming (SQP), that yields a closed-loop performance comparable to the full nonlinear MPC is also discussed. All of the controllers considered are tested in simulations that emulate a realistic test bench and their computational time is assessed on an industrial Programmable Logic Controller (PLC). Their performance is compared with standard industrial control in nominal and perturbed cases replicating the typical and critical disturbances and model mismatches. The numerical results show that the SQP and nonlinear MPC methods outperform the other controllers in the considered scenarios, based on closed-loop performance metrics for the surge margin, the reference tracking accuracy, and the system actuation, without significantly increasing the computational time.\u3c/p\u3