The Application of Artificial Intelligence in Project Management Research: A Review

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

Abstract:The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi

Adaptive Neuro-Fuzzy Inference System integrated with solar zenith angle for forecasting sub-tropical photosynthetically active radiation

Advocacy for climate mitigation aims to minimize the use of fossil fuel and to support clean energy adaptation. While alternative energies (e.g., biofuels) extracted from feedstock (e.g., micro‐algae) represent a promising role, their production requires reliably modeled photosynthetically active radiation (PAR). PAR models predict energy parameters (e.g., algal carbon fixation) to aid in decision‐making at PAR sites. Here, we model very short‐term (5‐min scale), sub‐tropical region's PAR with an Adaptive Neuro‐Fuzzy Inference System model with a Centroid‐Mean (ANFIS‐CM) trained with a non‐climate input (i.e., only the solar angle, θZ). Accuracy is benchmarked against genetic programming (GP), M5Tree, Random Forest (RF), and multiple linear regression (MLR). ANFIS‐CM integrates fuzzy and neural network algorithms, whereas GP adopts an evolutionary approach, M5Tree employs binary decision, RF employs a bootstrapped ensemble, and MLR uses statistical tools to link PAR with θZ. To design the ANFIS‐CM model, 5‐min θZ (01–31 December 2012; 0500H–1900H) for sub‐tropical, Toowoomba are utilized to extract predictive features, and the testing accuracy (i.e., differences between measurements and forecasts) is evaluated with correlation (r), root‐mean‐square error (RMSE), mean absolute error (MAE), Willmott (WI), Nash–Sutcliffe (ENS), and Legates & McCabes (ELM) Index. ANFIS‐CM and GP are equivalent for 5‐min forecasts, yielding the lowest RMSE (233.45 and 233.01μ mol m−2s−1) and MAE (186.59 and 186.23 μmol m−2s−1). In contrast, MLR, M5Tree, and RF yields higher RMSE and MAE [(RMSE = 322.25 μmol m−2s−1, MAE = 275.32 μmol m−2s−1), (RMSE = 287.70 μmol m−2s−1, MAE = 234.78 μmol m−2s−1), and (RMSE = 359.91 μmol m−2s−1, MAE = 324.52 μmol m−2s−1)]. Based on normalized error, ANFIS‐CM is considerably superior (MAE = 17.18% versus 19.78%, 34.37%, 26.39%, and 30.60% for GP, MLR, M5Tree, and RF models, respectively). For hourly forecasts, ANFIS‐CM outperforms all other methods (WI = 0.964 vs. 0.942, 0.955, 0.933 & 0.893, and ELM = 0.741 versus 0.701, 0.728, 0.619 & 0.490 for GP, MLR, M5Tree, and RF, respectively). Descriptive errors support the versatile predictive skills of the ANFIS‐CM model and its role in real‐time prediction of the photosynthetic‐active energy to explore biofuel generation from micro‐algae, studying food chains, and supporting agricultural precision

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research