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Records with Keyword: Artificial Intelligence
Integration of Artificial Intelligence into Biogas Plant Operation
Samet Cinar, Senem Onen Cinar, Nils Wieczorek, Ihsanullah Sohoo, Kerstin Kuchta
October 14, 2021 (v1)
Keywords: anaerobic digestion, Artificial Intelligence, automation, biogas plant, predictive monitoring, process monitoring, process optimization
In the biogas plants, organic material is converted to biogas under anaerobic conditions through physical and biochemical processes. From supply of the raw material to the arrival of the products to customers, there are serial processes which should be sufficiently monitored for optimizing the efficiency of the whole process. In particular, the anaerobic digestion process, which consists of sequential complex biological reactions, requires improved monitoring to prevent inhibition. Conventional implemented methods at the biogas plants are not adequate for monitoring the operational parameters and finding the correlation between them. As Artificial Intelligence has been integrated in different areas of life, the integration of it into the biogas production process will be inevitable for the future of the biogas plant operation. This review paper first examines the need for monitoring at the biogas plants with giving details about the process and process monitoring as well. In the follow... [more]
Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence
Konstantinos Paraskevoudis, Panagiotis Karayannis, Elias P. Koumoulos
May 27, 2021 (v1)
Keywords: 3D printing, additive manufacturing, Artificial Intelligence, computer vision, neural network
This work describes a novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision. Specifically, Neural Networks are developed for identifying 3D printing defects during the printing process by analyzing video captured from the process. Defects are likely to occur in 3D printed objects during the printing process, with one of them being stringing; they are mostly correlated to one of the printing parameters or the object’s geometries. The defect stringing can be on a large scale and is usually located in visible parts of the object by a capturing camera. In this case, an AI model (Deep Convolutional Neural Network) was trained on images where the stringing issue is clearly displayed and deployed in a live environment to make detections and predictions on a video camera feed. In this work, we present a methodology for developing and deploying deep neural networks for the recognition of... [more]
Processing, Characteristics and Composition of Umqombothi (a South African Traditional Beer)
Edwin Hlangwani, Janet Adeyinka Adebiyi, Wesley Doorsamy, Oluwafemi Ayodeji Adebo
May 26, 2021 (v1)
Keywords: Artificial Intelligence, bioprocessing, health, local beer, safety, South Africa, umqombothi
Traditional beers, such as palm wine, kombucha and others, are notable beverages consumed all over the globe. Such beverages historically contribute to food security on a global scale. Umqombothi is a South African traditional beer nutritionally packed with minerals, amino acids, B-group vitamins and much-needed calories. As a result, the production and consumption of this traditional beverage has been an integral part of South African’s social, economic and cultural prosperity. Unfortunately, difficulties in bioprocessing operations have limited its availability to household and small-scale production. It is at these micro-production scales that poor hygiene practices and the use of hazardous additives and contaminated raw materials continue to increase, posing serious health risks to the unassuming consumer. This study provides an overview of the processing steps and underlying techniques involved in the production of umqombothi, while highlighting the challenges as well as future de... [more]
Performance Evaluation for a Sustainable Supply Chain Management System in the Automotive Industry Using Artificial Intelligence
Oana Dumitrascu, Manuel Dumitrascu, Dan Dobrotǎ
May 24, 2021 (v1)
Keywords: Artificial Intelligence, data mining, key performance indicator, neural network, performance evaluation, risk management
Increasing the sustainability of a system can be achieved by evaluating the system, identifying the issues and their root cause and solving them. Performance evaluation translates into key performance indicators (KPIs) with a high impact on increasing overall efficacy and efficiency. As the pool of KPIs has increased over time in the context of evaluating the supply chain management (SCM) system’s performance and assessing, communicating and managing its risks, a mathematical model based on neural networks has been developed. The SCM system has been structured into subsystems with the most relevant KPIs for set subsystems and their most important contributions on the increase in the overall SCM system performance and sustainability. As a result of the performed research based on the interview method, the five most relevant KPIs of each SCM subsystem and the most relevant problems are underlined. The main goal of this paper is to develop a performance evaluation model that links specifi... [more]
Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy
Juan M. Sánchez, Juan P. Rodríguez, Helbert E. Espitia
May 17, 2021 (v1)
Keywords: agriculture, Artificial Intelligence, decision making, policy formulation, public policy
The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.
Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence
Yu Hou, Jimei Qi, Jiwei Hu, Yiqiu Xiang, Ling Xin, Xionghui Wei
June 23, 2020 (v1)
Subject: Materials
Keywords: Artificial Intelligence, ethyl violet, gradient boosted regression trees, mesoporous materials, Mn-doped Fe/rGO nanocomposites
Mesoporous Mn-doped Fe nanoparticle-modified reduced graphene oxide (Mn-doped Fe/rGO) was prepared through a one-step co-precipitation method, which was then used to eliminate ethyl violet (EV) in wastewater. The prepared Mn-doped Fe/rGO was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, high-resolution transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, N2-sorption, small angle X-ray diffraction and superconducting quantum interference device. The Brunauer−Emmett−Teller specific surface area of Mn-doped Fe/rGO composites was 104.088 m2/g. The EV elimination by Mn-doped Fe/rGO was modeled and optimized by artificial intelligence (AI) models (i.e., radial basis function network, random forest, artificial neural network genetic algorithm (ANN-GA) and particle swarm optimization). Among these AI models, ANN-GA is considered as the best model for predicting the removal efficiency of EV by Mn-doped Fe/rGO... [more]
Machine Learning-Based Prediction of a BOS Reactor Performance from Operating Parameters
Alireza Rahnama, Zushu Li, Seetharaman Sridhar
May 22, 2020 (v1)
Keywords: Artificial Intelligence, BOS reactor, Machine Learning, neural network, steelmaking
A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination o... [more]
Day-Ahead Prediction of Microgrid Electricity Demand Using a Hybrid Artificial Intelligence Model
Yuan-Jia Ma, Ming-Yue Zhai
August 5, 2019 (v1)
Keywords: Artificial Intelligence, electricity demand, feedforward artificial neural network, forecasting, microgrid, simulated annealing, smart grid, wavelet transform
Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target... [more]
Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model
Cihan Turhan, Silvio Simani, Ivan Zajic, Gulden Gokcen Akkurt
July 26, 2019 (v1)
Keywords: advanced control design, Artificial Intelligence, model-based and data-driven approaches, modelling and simulation for control, thermal unit nonlinear system
The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically⁻meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solution... [more]
Ultrasonic-Assisted Extraction and Swarm Intelligence for Calculating Optimum Values of Obtaining Boric Acid from Tincal Mineral
Bahdisen Gezer, Utku Kose
April 15, 2019 (v1)
Keywords: Artificial Intelligence, boric acid, central composite design, Optimization, swarm intelligence, tincal, ultrasound assisted extraction
The objective of this study is to focus on boric acid extraction from the mineral tincal, in order to determine the optimum conditions thanks to the ultrasonic-assisted extraction (UAE) technique (with the response surface methodology (RSM) for the first time), and artificial intelligence based swarm intelligence. Characterization of the tincal were done by using thermo-gravimetric assay (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. In detail, a central composite design (CCD) was used for determining the effects of different solvent/solid ratios, pH, extraction time, and extraction temperature on the yield, which was determined by the conductometric method. The optimum values regarding the best extraction process was calculated by using five different swarm intelligence techniques: Particle swarm optimization (PSO), cuckoo search (CS), genetic algorithms (GA), Differential evolution (DE), and the vortex optimization algorithm (VOA). In... [more]
Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions
Abdullahi Abubakar Mas’ud, Ricardo Albarracín, Jorge Alfredo Ardila-Rey, Firdaus Muhammad-Sukki, Hazlee Azil Illias, Nurul Aini Bani, Abu Bakar Munir
January 7, 2019 (v1)
Keywords: Artificial Intelligence, artificial neural network (ANN), partial discharge (PD)
In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) usin... [more]
Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset
Ariana Moncada, Walter Richardson Jr, Rolando Vega-Avila
September 21, 2018 (v1)
Keywords: all-sky imaging, Artificial Intelligence, decision tree learning, deep learning, optical flow, solar irradiance forecasting
Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.
Deterministic Global Optimization with Artificial Neural Networks Embedded
Global deterministische Optimierung von Optimierungsproblemen mit künstlichen neuronalen Netzwerken
Artur M Schweidtmann, Alexander Mitsos
October 15, 2018 (v2)
Subject: Optimization
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.
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