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.

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.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Energy use of buildings at urban scale: A case study of london school buildings

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.

A comparison of winter pre-heating requirements for natural displacement and natural mixing ventilation

In winter, natural ventilation can be achieved either through mixing ventilation or upward displacement ventilation (P.F. Linden, The fluid mechanics of natural ventilation, Annual Review of Fluid Mechanics 31 (1999) pp. 201-238). We show there is a significant energy saving possible by using mixing ventilation, in the case that the internal heat gains are significant, and illustrate these savings using an idealized model, which predicts that with internal heat gains of order 0.1 kW per person, mixing ventilation uses of a fraction of order 0.2-0.4 of the heat load of displacement ventilation assuming a well-insulated building. We then describe a strategy for such mixing natural ventilation in an atrium style building in which the rooms surrounding the atrium are able to vent directly to the exterior and also through the atrium to the exterior. The results are motivated by the desire to reduce the energy burden in large public buildings such as hospitals, schools or office buildings centred on atria. We illustrate a strategy for the natural mixing ventilation in order that the rooms surrounding the atrium receive both pre-heated but also sufficiently fresh air, while the central atrium zone remains warm. We test the principles with some laboratory experiments in which a model air chamber is ventilated using both mixing and displacement ventilation, and compare the energy loads in each case. We conclude with a discussion of the potential applications of the approach within the context of open plan atria type office buildings.

Specifying a hydraulic regenerative braking system for an articulated urban delivery vehicle

Previous research has shown that hydraulic systems offer potentially the lightest and smallest regenerative braking technology for heavy goods vehicles. This paper takes the most practical embodiment of a hydraulic system for an articulated urban delivery vehicle and investigates the best specification for the various components, based on a simulated stop-start cycle. The potential energy saving is quantified. © 2011 IEEE.

Energy recovery from high-frequency clocks using DC-DC converters

Large digital chips use a significant amount of energy to distribute a multi-GHz clock. By discharging the clock network to ground every cycle, the energy stored in this large capacitor is wasted. Instead, the energy can be recovered using an on-chip DC-DC converter. This paper investigates the integration of two DC-DC converter topologies, boost and buck-boost, with a high-speed clock driver. The high operating frequency significantly shrinks the required size of the L and C components so they can be placed on-chip; typical converters place them off-chip. The clock driver and DC-DC converter are able to share the entire tapered buffer chain, including the widest drive transistors in the final stage. To achieve voltage regulation, the clock duty cycle must be modulated; implying only single-edge-triggered flops should be used. However, this minor drawback is eclipsed by the benefits: by recovering energy from the clock, the output power can actually exceed the additional power needed to operate the converter circuitry, resulting in an effective efficiency greater than 100%. Furthermore, the converter output can be used to operate additional power-saving features like low-voltage islands or body bias voltages. ©2008 IEEE.