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.

Multiobjective design optimization of IGBT power modules considering power cycling and thermal cycling

Insulated-gate bipolar transistor (IGBT) power modules find widespread use in numerous power conversion applications where their reliability is of significant concern. Standard IGBT modules are fabricated for general-purpose applications while little has been designed for bespoke applications. However, conventional design of IGBTs can be improved by the multiobjective optimization technique. This paper proposes a novel design method to consider die-attachment solder failures induced by short power cycling and baseplate solder fatigue induced by the thermal cycling which are among major failure mechanisms of IGBTs. Thermal resistance is calculated analytically and the plastic work design is obtained with a high-fidelity finite-element model, which has been validated experimentally. The objective of minimizing the plastic work and constrain functions is formulated by the surrogate model. The nondominated sorting genetic algorithm-II is used to search for the Pareto-optimal solutions and the best design. The result of this combination generates an effective approach to optimize the physical structure of power electronic modules, taking account of historical environmental and operational conditions in the field.

Instance generators and test suites for the multiobjective quadratic assignment problem

We describe, and make publicly available, two problem instance generators for a multiobjective version of the well-known quadratic assignment problem (QAP). The generators allow a number of instance parameters to be set, including those controlling epistasis and inter-objective correlations. Based on these generators, several initial test suites are provided and described. For each test instance we measure some global properties and, for the smallest ones, make some initial observations of the Pareto optimal sets/fronts. Our purpose in providing these tools is to facilitate the ongoing study of problem structure in multiobjective (combinatorial) optimization, and its effects on search landscape and algorithm performance.

A Diagnostic Assessment Of Evolutionary Multiobjective Optimization For Water Resources Systems

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

Multiobjective 0-1 integer programming for the use of sugarcane residual biomass in energy cogeneration

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multiobjective optimization of economic balances of sugarcane harvest biomass

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluating distributed generation impacts with a multiobjective index

Evaluating the technical impacts associated with connecting distributed generation to distribution networks is a complex activity requiring a wide range of network operational and security effects to be qualified and quantified. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and which could be used to shape the nature of the contract between the utility and distributed generator. This paper presents a multiobjective performance index for distribution networks with distributed generation which considers a wide range of technical issues. Distributed generation is extensively located and sized within the IEEE-34 test feeder, wherein the multiobjective performance index is computed for each configuration. The results are presented and discussed.

Multiobjective linear model for pre-feasibility design of cogeneration systems

This article deals with some methodologies for economic and technical evaluations of cogeneration projects proposed by several authors. A discussion on design philosophy applied to thermal power plants leads to the decision problem of a conflicting, multiobjective formulation that includes the most important parameters. This model is formulated to help decision makers and designers in choosing compromise values for included parameters. (C) 1997 Elsevier B.V. Ltd.

Evaluating distributed time-varying generation through a multiobjective index

In the last decade, distributed generation, with its various technologies, has increased its presence in the energy mix presenting distribution networks with challenges in terms of evaluating the technical impacts that require a wide range of network operational effects to be qualified and quantified. The inherent time-varying behavior of demand and distributed generation (particularly when renewable sources are used), need to be taken into account since considering critical scenarios of loading and generation may mask the impacts. One means of dealing with such complexity is through the use of indices that indicate the benefit or otherwise of connections at a given location and for a given horizon. This paper presents a multiobjective performance index for distribution networks with time-varying distributed generation which consider a number of technical issues. The approach has been applied to a medium voltage distribution network considering hourly demand and wind speeds. Results show that this proposal has a better response to the natural behavior of loads and generation than solely considering a single operation scenario.

A class of multiobjective control problems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of a multiobjective performance index for distribution systems with distributed generation

Here a multiobjective performance index for distribution systems with distributed generation based on a steady-state analysis of the network is proposed. This index quantifies the distributed generation impact on total losses, voltage profile and short circuit currents, and will be used as objective function in an evolutionary algorithm aimed at searching the best points for connecting distributed generators. Moreover, a loss allocation technique, based on the Zbus method, is applied on the original configuration of the network to obtain a good quality initial population. An IEEE medium voltage distribution network is analysed and results are presented and discussed.