Implementation of Discrete Capability into the enhanced Multipoint Approximation Method for solving mixed integer-continuous optimization problems

Multipoint approximation method (MAM) focuses on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem within a trust region. To develop an optimization technique applicable to mixed integer-continuous design optimization problems in which the objective and constraint functions are computationally expensive and could be impossible to evaluate at some combinations of design variables, a simple and efficient algorithm, coordinate search, is implemented in the MAM. This discrete optimization capability is examined by the well established benchmark problem and its effectiveness is also evaluated as the discreteness interval for discrete design variables is increased from 0.2 to 1. Furthermore, an application to the optimization of a lattice composite fuselage structure where one of design variables (number of helical ribs) is integer is also presented to demonstrate the efficiency of this capability

Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach

The operation of complex systems can drift away from the initial design conditions, due to environmental conditions, equipment wear or specific restrictions. Steam generators are complex equipment and their proper operation relies on the identification of their most relevant parameters. An approach to rank the operational parameters of a subcritical steam generator of an actual 360 MW power plant is presented. An Artificial Neural Network - ANN delivers a model to estimate the steam generator efficiency, electric power generation and flue gas outlet temperature as a function of seven input parameters. The ANN is trained with a two-year long database, with training errors of 0.2015 and 0.2741 (mean absolute and square error) and validation errors of 0.32% and 2.350 (mean percent and square error). That ANN model is explored by means of a combination of situations proposed by a Design of Experiment - DoE approach. All seven controlled parameters showed to be relevant to express both steam generator efficiency and electric power generation, while primary air flow rate and speed of the dynamic classifier can be neglected to calculate flue gas temperature as they are not statistically significant. DoE also shows the prominence of the primary air pressure in respect to the steam generator efficiency, electric power generation and the coal mass flow rate for the calculation of the flue gas outlet temperature. The ANN and DoE combined methodology shows to be promising to enhance complex system efficiency and helpful whenever a biased behavior must be brought back to stable operation

Making the most out of surrogate models: ticks of the trade

International audienceno abstrac

Making the most out of surrogate models: ticks of the trade

International audienceno abstrac

Operational guide to stabilize, standardize and increase power plant efficiency

Complex engineering systems, such as power plants, deliver their best performance when operating along a designed range of some priority parameters. However, plant field operation may deviate from design conditions, and new references must be identified. Actions towards high-quality operation can be supported by fine modeling, which helps building decision support tools. The present work proposes a standardization strategy for the operation of an actual coal-fired power plant based on a Design of Experiment approach, partially tested onsite and finally accomplished with surrogate models built upon a 2 year long database. Artificial Neural Networks (ANNs) and Mass and Energy balances (M&Es) are used to represent the plant’s steam generator and its mills subset, which is the core of an operational guide to increase system efficiency under actual operation. Primary and secondary air flows, pulverized coal outlet temperature, speed of the dynamic classifier, primary air flow, excess O, primary and secondary air pressures are the seven controllable factors selected as the most relevant ones among an extensive set of parameters, able to perform effective maneuvers. The application of the operational guide indicates combinations of ranges of the seven controllable parameters that allow for achieving steam generator efficiency within the 84.0% to 88.92% range. The proposed methodology aims as well to improve safe and stable conditions to a system that undergoes operation different than the one prescribed by the original design. The study case results show an opportunity to raise efficiency by up to 2.28% during operation, which represents a reduction in coal consumption by 3.1 t/h and above 6% on CO emissions