129 resultados para investment efficiency


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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.

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The self-organization of the helical structure of chiral nematic liquid crystals combined with their sensitivity to electric fields makes them particularly interesting for low-threshold, wavelength tunable laser devices. We have studied these organic lasers in detail, ranging from the influence specific macroscopic properties, such as birefringence and order parameter, have on the output characteristics, to practical systems in the form of two-dimensional arrays, double-pass geometries and paintable lasers. Furthermore, even though chiral nematics are responsive to electric fields there is no facile means by which the helix periodicity can be adjusted, thereby allowing laser wavelength tuning, without adversely affecting the optical quality of the resonator. Therefore, in addition to studying the liquid crystal lasers, we have focused on finding a novel method with which to alter the periodicity of a chiral nematic using electric fields without inducing defects and degrading the optical quality factor of the resonator. This paper presents an overview of our research, describing (i) the correlation between laser output and material properties,(ii) the importance of the gain medium,(iii) multicolor laser arrays, and (iv) high slope efficiency (>60%) silicon back-plane devices. Overall we conclude that these materials have great potential for use in versatile organic laser systems.

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Just under half of all energy consumption in the UK today takes place indoors, and over a quarter within our homes. The challenges associated with energy security, climate change and sustainable consumption will be overcome or lost in existing buildings. A background analysis, and the scale of the engineering challenge for the next three to four decades, is described in this paper.

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Just under half of all energy consumption in the UK today takes place indoors, and over a quarter within our homes. The challenges associated with energy security, climate change and sustainable consumption will be overcome or lost in our existing buildings. A background analysis, and the scale of the engineering challenge for the next three to four decades, is described in this paper.

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This paper presents the initial results of on-going research in the field of external Corporate Venture Capital (CVC) investments, i.e. equity investments of large corporations in entrepreneurial ventures which originated outside the corporation. The research is motivated by the fact that external CVC plays an increasingly important role within the strategy of corporations. Driven by a general trend towards a more open approach to innovation, companies see particular value in external corporate venturing as a tool to gain, for example, access to complementary technologies and a general window on technology developments. The review of literature in the field of external corporate venturing clearly reveals that theoretical gaps exist in understanding mechanisms for capturing value and measurements of this value. To help close these gaps, the research addresses the underlying question "How do corporations and start-ups capture and measure strategic value through external CVC investments" by using embedded, multiple case studies. Following an initial set of case studies, steps towards the development of a framework for capturing and measuring strategic value from CVC investments are outlined within this paper and the resulting preliminary framework is presented. The paper closes with an outlook on ongoing and future research steps. © 2009 PICMET.

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Deciding which technology to invest in is a recurring issue for technology managers, and the ability to successfully identify the right technology can be a make or break decision for a company. The effects of globalisation have made this issue even more imperative. Not only do companies have to be competitive by global standards but increasingly they have to source technological capabilities from overseas as well. Technology managers already have a variety of decision aids to draw upon, including valuation tools, for example DCF and real options; decision trees; and technology roadmapping. However little theory exists on when, where, why or even how to best apply particular decision aids. Rather than developing further techniques, this paper reviews the relevance and limitations of existing techniques. This is drawn from an on going research project which seeks to support technology managers in selecting and applying existing decision aids and potentially in the design of future decision aids. It is intended that through improving the selection of decision aids, decision performance can be increased, leading to more effective allocation of resources and hence competitive advantage. (c) 2006 PICMET.

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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.