Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

Improved energy efficiency in paper making through reducing dryer steam consumption using advanced process control

Over the last two or three years, the increasing costs of energy and worsening market conditions have focussed even greater attention within paper mills than before, on considering ways to improve efficiency and reduce the energy used in paper making. Arising from a multivariable understanding of paper machine operation, Advanced Process Control (APC) technology enables paper machine behaviour to be controlled in a more coherent way, using all the variables available for control. Furthermore, with the machine under better regulation and with more variables used in control, there is the opportunity to optimise machine operation, usually providing very striking multi-objective performance improvement benefits of a number of kinds. Traditional three term control technology does not offer this capability. The paper presents results from several different paper machine projects we have undertaken around the world. These projects have been aimed at improving machine stability, optimising chemicals usage and reducing energy use. On a brown paperboard machine in Australasia, APC has reduced specific steam usage by 10%, averaged across the grades; the controller has also provided a significant capacity to increase production. On a North American newsprint machine, the APC system has reduced steam usage by more than 10%, and it provides better control of colour and much improved wet end stability. The paper also outlines early results from two other performance improvement projects, each incorporating a different approach to reducing the energy used in paper making. The first of these two projects is focussed on optimising sheet drainage, aiming to present the dryer with a sheet having higher solids content than before. The second project aims to reduce specific steam usage by optimising the operation of the dryer hood.

Robust stabilization of a nonlinear cement mill model

Plugging is well known to be a major cause of instability in industrial cement mills. A simple nonlinear model able to simulate the plugging phenomenon is presented. It is shown how a nonlinear robust controller can be designed in order to fully prevent the mill from plugging.

Fibre optic monitoring of a deep circular excavation

This paper describes part of the monitoring undertaken at Abbey Mills shaft F, one of the main shafts of Thames Water's Lee tunnel project in London, UK. This shaft, with an external diameter of 30 m and 73 m deep, is one of the largest ever constructed in the UK and consequently penetrates layered and challenging ground conditions (Terrace Gravel, London Clay, Lambeth Group, Thanet Sand Formation, Chalk Formation). Three out of the twenty 1-2 m thick and 84 m deep diaphragm wall panels were equipped with fibre optic instrumentation. Bending and circumferential hoop strains were measured using Brillouin optical time-domain reflectometry and analysis technologies. These measurements showed that the overall radial movement of the wall was very small. Prior to excavation during a dewatering trial, the shaft may have experienced three-dimensional deformation due to differential water pressures. During excavation, the measured hoop and bending strains of the wall in the chalk exceeded the predictions. This appears to be related to the verticality tolerances of the diaphragm wall and lower circumferential hoop stiffness of the diaphragm walls at deep depths. The findings from this case study provide valuable information for future deep shafts in London. © ICE Publishing: All rights reserved.