18,936 research outputs found
Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant
The energy efficiency of industrial plants is an important issue in any type of business but particularly in
the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more
as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring
better product quality, and generally increasing profits. This article describes a decision system developed
for optimizing the energy efficiency of a petrochemical plant. The system has been developed after
a data mining process of the parameters registered in the past. The designed system carries out an optimization
process of the energy efficiency of the plant based on a combined algorithm that uses the following
for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in
the past and, on the other hand, a module of two neural networks to obtain new interpolated operation
points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to
select the parameters most important in the plant and to study the behavior of the energy efficiency
index. This study also helped ensure an optimal training of the neural networks. The robustness of the
system as well as its satisfactory results in the testing process (an average rise in the energy efficiency
of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to
implement and to integrate the decision system as a pilot software in an SCADA
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants
An effective modeling technique is proposed for determining baseline energy consumption in the industry.
A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation
of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption
and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the
current consumption and production in the event that no energy-saving measures had been implemented.
Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial
neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable
accuracy levels of prediction are detected, confirming good capability of the models for predicting plant
behavior and their suitability for baseline energy consumption determining purposes. High level of robustness
is observed for ANN against uncertainty affecting measured values of variables used as input in the
models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive
industry. Application of ANN technique would also help to overcome the limited availability of
on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants
The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding
from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the
European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy,
Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL
(KK-2018/00096), and by Ministerio de EconomĂa y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)
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