Artificial Intelligence driven smart operation of large industrial complexes supporting the net-zero goal: Coal power plants

The true potential of artificial intelligence (AI) is to contribute towards the performance enhancement and informed decision making for the operation of the large industrial complexes like coal power plants. In this paper, AI based modelling and optimization framework is developed and deployed for the smart and efficient operation of a 660 MW supercritical coal power plant. The industrial data under various power generation capacity of the plant is collected, visualized, processed and subsequently, utilized to train artificial neural network (ANN) model for predicting the power generation. The ANN model presents good predictability and generalization performance in external validation test with R2 = 0.99 and RMSE =2.69 MW. The partial derivative of the ANN model is taken with respect to the input variable to evaluate the variable’ sensitivity on the power generation. It is found that main steam flow rate is the most significant variable having percentage significance value of 75.3 %. Nonlinear programming (NLP) technique is applied to maximize the power generation. The NLP-simulated optimized values of the input variables are verified on the power generation operation. The plant-level performance indicators are improved under optimum operating mode of power generation: savings in fuel consumption (3 t/h), improvement in thermal efficiency (1.3 %) and reduction in emissions discharge (50.5 kt/y). It is also investigated that maximum power production capacity of the plant is reduced from 660 MW to 635 MW when the emissions discharge limit is changed from 510 t/h to 470 t/h. It is concluded that the improved plant-level performance indicators and informed decision making present the potential of AI based modelling and optimization analysis to reliably contribute to net-zero goal from the coal power plant

Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality

The role of carbon capture technology using monoethanolamine (MEA) is critical for achieving the carbon-neutrality goal. However, maintaining the efficient operation of the post-combustion carbon capture is challenging considering the hyperdimensional design space and nonlinear characteristics of the process. In this work, CO2 capture level from the flue gas in the absorption column is investigated for the post-combustion carbon capture process using MEA. Artificial neural network (ANN) and support vector machine (SVM) models are constructed to model CO2 capture level under extensive hyperparameters tuning. The comparative performance analysis based on external validation test confirmed the superior modelling and generalization ability of ANN for the carbon capture process. Later, partial derivative-based sensitivity analysis is carried out and it is the found that absorbent-based input variables like lean solvent temperature and lean solvent flow rate are the two most significant input variables on CO2 capture level in the absorption column. The optimization problem with the ANN model embedded in the nonlinear programming-based optimization environment is solved under different operating scenarios to determine the optimum operating ranges for the input variables corresponding to the maximum CO2 capture level. This research presents the optimum operating conditions for CO2 removal from the flue gas for the post-combustion carbon capture process using MEA that contributes to achieving the carbon neutrality goal

Artificial intelligence–built analysis framework for the manufacturing sector: performance optimization of wire electric discharge machining system

In the era of industry 4.0, digitalization and smart operation of industrial systems contribute to higher productivity, improved quality, and efficient resource utilization for industrial operations and processes. However, artificial intelligence (AI)–based modelling and optimization analysis following a generic analysis framework is lacking in literature for the manufacturing sector thereby impeding the inclusion of AI for its potential application's domain. Herein, a comprehensive and generic analysis framework is presented depicting the key stages involved for carrying out the AI-based modelling and optimization analysis for the manufacturing system. The suggested AI framework is put into practice on wire electric discharge machining (WEDM) system, and the cutting speed of WEDM is adjusted for the stainless cladding steel material. Artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) are three AI modelling techniques that are trained with meticulous hyperparameter tuning. A better-performing model is chosen once the trained AI models have undergone the external validation test to investigate their prediction performance. The sensitivity analysis on the developed AI model is performed and it is found that pulse on time (Pon) is the noteworthy factor affecting the cutting speed of WEDM having the percentage significance value of 26.6 followed by the Dw and LTSS, with the percentage significance value of 17.3 and 16.7 respectively. The parametric optimization incorporating the AI model is conducted and the results pertain to the cutting speed are 27.3% higher than the maximum value of cutting speed achieved for WEDM. The cutting speed performance optimization is realized following the proposed AI-based analysis framework that can be applied, in general, to other manufacturing systems therefore unlocking the potential of AI to contribute to industry 4.0 for the smart operation of manufacturing systems

The cientificWorldJOURNAL Research Article Levels of Heavy Metals in Popular Cigarette Brands and Exposure to These Metals via Smoking

The levels of selected heavy metals in popular cigarette brands sold and/or produced in Saudi Arabia were determined by graphite furnace-atomic absorption spectrometry (GFAAS). Average concentrations of Cadmium and Lead in different cigarette brands were 1.81 and 2.46 µg g −1 (dry weight), respectively. The results obtained in this study estimate the average quantity of Cd inhaled from smoking one packet of 20 cigarettes to be in the range of 0.22-0.78 µg. Results suggest that the quantity of Pb inhaled of smoking one packet of 20 cigarettes is estimated to be 0.97-2.64 µg. The concentrations of Cd and Pb in cigarettes were significantly different between cigarette brands tested. The results of the present study were compared with those of other regional and international studies

Sustainable EDM of Inconel 600 in Cu-mixed biodegradable dielectrics: Modelling and optimizing the process by artificial neural network for supporting net-zero from industry

The properties of Nickel-based superalloy(s) like stability at extreme conditions, greater strength, etc., complicate its cutting through conventional operations. Therefore, electric discharge machining (EDM) is preferred for its accurate cutting. However, the conventional dielectric i.e., kerosene used in EDM is hydrocarbon based which generates toxic fumes and contribute to the CO2 emissions during the discharging process in EDM. This affects the operator’s health and the environment. Therefore, the potentiality of five biodegradable dielectrics has been deeply examined herein to address the said issues. Nano copper powder is also employed for uplifting the cutting proficiency of these dielectrics. A set of 15 experiments was performed via full factorial design. An artificial neural network (ANN) is constructed to model and optimize the material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC). The highest MRR (5.527 mm3 /min) was achieved in coconut oil whereas for obtaining the lowest SR, the sunflower oil at powder concentration (Cp) of 1.0 g/100 ml is the best choice. Sunflower oil also gave a 17.05% better surface finish compared to other dielectrics. Amongst the biodegradable dielectrics, olive oil consumes lowest specific energy (SEC) i.e., 264.16 J/mm3 which is 28.8% less than the SEC of other oils. Furthermore, the maximum CO2 reduction of 72.8 ± 1.4% is achieved with Olive oil in comparison to that found with kerosene in EDM. The multi-objective optimization is conducted and sunflower oil with Cp of 0.667 g/100 ml is termed out to be optimal solution. The biodegradable dielectrics have demonstrated excellent performance for EDM to support net-zero goals from the industrial sector

Modeling Wastewater Evolution and Management Options under Variable Land Use Scenarios

The development of a reliable decision support system and predictions for water quantity and quality often require a reasonable level of environmental and hydrological simulations at various geographic scales. The Soil and Water Assessment Tool (SWAT) model offers distributed parameter and continuous time simulation, and flexible watershed configuration and with the adoption of geographic information system (GIS) technology, a user-friendly and interactive decision support system can be developed for wastewater management. In this chapter, we evaluated the spatio-temporal evolution of wastewater contaminants in an environmentally degraded watershed through integrated field-based investigations and modeling approach. Later, management options were identified to improve the watershed health and agro-environment. The results of the modeling study exhibited variable responses of surface runoff and water quality to different scenarios of land use change. Temporal wastewater analysis indicated a significant impact of seasonality on the contaminants’ population levels. The adopted approach would prove effective in evaluating better management options to reduce negative impacts of wastewater and contaminants for sustainable agro-environment in future

Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning-based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block-2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI-based models. The best is the Ridge Regressor, a high-level end-to-end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 μm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC’s nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments

Hydrogen Production Using TiO2-Based Photocatalysts: A Comprehensive Review

Titanium dioxide (TiO2) is one of the most widely used photocatalysts due to its physical and chemical properties. In this study, hydrogen energy production using TiO2- and titanate-based photocatalysts is discussed along with the pros and cons. The mechanism of the photocatalysis has been elaborated to pinpoint the photocatalyst for better performance. The chief characteristics and limitations of the TiO2 photocatalysts have been assessed. Further, TiO2-based photocatalysts modified with a transition metal, transition metal oxide, noble metal, graphitic carbon nitride, graphene, etc. have been reviewed. This study will provide a basic understanding to beginners and detailed knowledge to experts in the field to optimize the TiO2-based photocatalysts for hydrogen production

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed