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1. |
Record Nr. |
UNINA9911007371103321 |
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Autore |
Kroger Detlev G. |
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Titolo |
Air-cooled heat exchangers and cooling towers : thermal-flow performance evaluation and design. Volume II / / by Detlev G. Kroger |
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Pubbl/distr/stampa |
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Tulsa, Okla., : PennWell, 2004 |
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ISBN |
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1-62870-299-0 |
9781593700195 |
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Descrizione fisica |
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1 online resource (460 p.) |
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Disciplina |
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Soggetti |
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Factories - Cooling |
Petroleum refineries - Cooling |
Electric power-plants - Cooling |
Heat - Transmission |
Refrigeration and refrigerating machinery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
EBL Purchase. |
1 concurrent user. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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6. Fans -- 7. Natural draft cooling towers -- 8. Mechanical draft coolers -- 9. Meteorological effects -- 10. Cooling system selection and optimization. |
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Sommario/riassunto |
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This new text represents the most detailed and comprehensive book presenting modern practice and theory relevant to the thermal-flow performance evaluation, design, and optimization of air-cooled heat exchangers and cooling towers. Kroger provides modern analytical and empirical tools used to evaluate the thermal-flow performance and design of air-cooled heat exchangers and cooling towers. He also covers how to prepare improved specifications and evaluate more critical bids with respect to thermal performance of new cooling systems. Further, Kroger explores improvement possibilities with respect to retrofits of existing cooling units as well as possible impacts of plant operations and environmental influences. This book lets you: optimize plant efficiency through an understanding of key reasons for poor performance; get extensive up-to-date information on air-cooled |
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heat exchangers and cooling towers; and, reduce misunderstanding between supplier and client through increased insight and intelligent specifications understanding. |
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2. |
Record Nr. |
UNINA9911018749503321 |
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Autore |
Valenzuela Olga |
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Titolo |
Time Series Analysis and Forecasting : Selected Contributions from ITISE 2023 / / edited by Olga Valenzuela, Fernando Rojas, Luis Javier Herrera, Héctor Pomares, Ignacio Rojas |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (503 pages) |
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Collana |
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Contributions to Statistics, , 2628-8966 |
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Altri autori (Persone) |
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RojasFernando |
HerreraLuis Javier |
PomaresHéctor |
RojasIgnacio |
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Disciplina |
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Soggetti |
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Time-series analysis |
Econometrics |
Statistics |
Computer science - Mathematics |
Mathematical statistics |
Time Series Analysis |
Statistics in Business, Management, Economics, Finance, Insurance |
Probability and Statistics in Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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- Part I Advanced Econometric Methods -- Banking sector development and economic growth in developing countries. Does the change in the shadow economy matter? A Nonlinear Panel ARDL -- Improving the prediction of Norwegian household consumption by adjusting for temporary fluctuations in dividend income -- Inflation expectations change during the pre-war and war period. A comparison of Ukraine |
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and neighboring economies -- Analysis of diversification in investment portfolios Return and Risk for different time horizons -- Economic Diversity and the Dutch Disease in Angola -- Part II Artificial Intelligence and Time Series -- Increasing the Performance and Plausibility of Machine Learning via Data Analysis Techniques -- Combining Forecasts of Time Series with Complex Seasonality using LSTM-based Meta-Learning -- Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-distributed Errors -- Forecasting of the F10.7 solar radio index: A Multivariate Deep Learning Approach -- Part III Financial Forecasting and Risk Analysis -- Risk-adjusted Returns of Croatian Largest Manufacturers and Their Determinants -- Usage of portfolio replication in non-life insurance -- Encoding Stock Returns Relationships via Latent Embeddings for Enhanced Portfolio Optimization -- A Measure of Bivariate Long Memories in Financial Time Series with Applications to Granger Causality Networks -- Volatility-inspired σ-LSTM cell -- Part IV Theoretical Aspects of Time Series -- Bayesian Analysis of Systemic Risks Distributions -- Empirical function-based time series analysis for high-dimensional ground motion data: A focus on nonstationary and nonlinear phenomena -- Extended Research on Categorical Data Encoding Techniques for Recursive Multi-Step Prediction of Vessel Trajectory -- Part V Time Series Analysis Applications -- Predicting Safety- Critical Events in Traffic Flow Based on Time-Series -- Two-Factor and ARIMA-LS-SVR Models for Forecasting of EUA Futures Prices -- Interest Rate Sensitivity of the largest European Pharmaceutical Companies. An Extension of The Fama and French Five-Factor Model. |
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Sommario/riassunto |
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This volume on the latest developments in the theory and applications of time series analysis and forecasting comprises a selection of refereed papers presented at the 9th International Conference on Time Series and Forecasting, ITISE 2023, held in Gran Canaria, Spain, July 12-14, 2023. It is divided into several parts that address modern theoretical aspects of time series analysis, advanced econometric methods, time series and machine learning, financial forecasting and risk analysis, and applications to various disciplines, including econometrics and energy research. The broad range of topics and applications presented, including matters of particular relevance for sustainable development, gives readers a modern perspective on the subject. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics. |
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