Fuzzy modeling and stable model predictive tracking control of large-scale power plants

The Shuaiba Industrial Area (SIA) is located about 50 km south of Kuwait City. It accommodates most of the large-scale industries in Kuwait. The total area of the SIA (both eastern and western sectors) is about 22.98 million m2. Fifteen plants are located in the eastern sector and 23 in the western sector, including two petrochemical companies, three refineries, two power plants, a melamine company, an industrial gas corporation, a paper products company and, two steam electricity generating stations, in addition to several other industries. Therefore, only 30 percent of the land in the SIA's eastern sector and 70 percent of land in the SIA's western sector is available for future expansion. Presently, industries in the SIA generate approximately 204,000 t of solid waste. With future development in the industries in the SIA, the estimated quantities will reach 240,000 t. The Shuaiba Area Authority (SAA), a governmental regulatory body responsible for planning and development in the SIA, has recognized the problem of solid waste and has developed an industrial waste minimization program. This program would help to reduce the quantity of waste generated within the SIA and thereby reduce the cost of waste management. This paper presents a description of the waste minimization program and how it is to be implemented by major petroleum companies. The protocols employed in the waste minimization program are detailed.

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Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽

10.3390/atmos12070811 ◽

2021 ◽

Vol 12 (7) ◽

pp. 811

Author(s):

Yaqin Hu ◽

Yusheng Shi

Keyword(s):

Power Plant ◽

Co2 Emissions ◽

Atmospheric Carbon Dioxide ◽

Power Plants ◽

Large Scale ◽

Global Climate ◽

Gaussian Model ◽

Point Sources ◽

Emission Inventories ◽

Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

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On the Root Causes of the Fukushima Daiichi Disaster from the Perspective of High Complexity and Tight Coupling in Large-Scale Systems

Symmetry ◽

10.3390/sym13030414 ◽

2021 ◽

Vol 13 (3) ◽

pp. 414

Author(s):

Atsuo Murata ◽

Waldemar Karwowski

Keyword(s):

Nuclear Power ◽

Power Plants ◽

Large Scale ◽

High Complexity ◽

Tight Coupling ◽

Large Scale Systems ◽

Safety Issues ◽

Loss Of Coolant ◽

Station Blackout ◽

Fukushima Daiichi

This study explores the root causes of the Fukushima Daiichi disaster and discusses how the complexity and tight coupling in large-scale systems should be reduced under emergencies such as station blackout (SBO) to prevent future disasters. First, on the basis of a summary of the published literature on the Fukushima Daiichi disaster, we found that the direct causes (i.e., malfunctions and problems) included overlooking the loss of coolant and the nuclear reactor’s failure to cool down. Second, we verified that two characteristics proposed in “normal accident” theory—high complexity and tight coupling—underlay each of the direct causes. These two characteristics were found to have made emergency management more challenging. We discuss how such disasters in large-scale systems with high complexity and tight coupling could be prevented through an organizational and managerial approach that can remove asymmetry of authority and information and foster a climate of openly discussing critical safety issues in nuclear power plants.

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A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽

10.3390/en14051261 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1261

Author(s):

Christopher Gradwohl ◽

Vesna Dimitrievska ◽

Federico Pittino ◽

Wolfgang Muehleisen ◽

András Montvay ◽

...

Keyword(s):

Power Plants ◽

Large Scale ◽

Failure Detection ◽

Energy Yield ◽

Combined Approach ◽

Levelized Cost Of Electricity ◽

Term Operation ◽

Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

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Hydrogen energy and large scale hydrogen production with nuclear power plants based on high-temperature reactors

Journal of Physics Conference Series ◽

10.1088/1742-6596/1683/4/042031 ◽

2020 ◽

Vol 1683 ◽

pp. 042031

Author(s):

V V Petrunin ◽

I V Marov ◽

N G Kodochigov

Keyword(s):

High Temperature ◽

Hydrogen Production ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Large Scale ◽

Hydrogen Energy ◽

High Temperature Reactors

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Sizing and Management of Energy Storage Systems in Large-Scale Power Plants Using Price Control and Artificial Intelligence

Energies ◽

10.3390/en14113296 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3296

Author(s):

Carlos García-Santacruz ◽

Luis Galván ◽

Juan M. Carrasco ◽

Eduardo Galván

Keyword(s):

Energy Storage ◽

Power Plants ◽

Large Scale ◽

Storage Systems ◽

Renewable Energy Sources ◽

Ancillary Services ◽

Energy Sources ◽

Energy Storage Systems ◽

Electric System ◽

Fundamental Part

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.

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Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219834611 ◽

2019 ◽

Vol 41 (13) ◽

pp. 3612-3625 ◽

Cited By ~ 1

Author(s):

Wang Qian ◽

Wang Qiangde ◽

Wei Chunling ◽

Zhang Zhengqiang

Keyword(s):

Neural Networks ◽

Tracking Control ◽

Large Scale ◽

Inverse Dynamics ◽

Small Neighborhood ◽

High Order ◽

Interconnected Systems ◽

Delay Systems ◽

Rbf Neural Networks ◽

Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

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Multi-scale modelling of a large scale shell-and-tube latent heat storage system for direct steam generation power plants

10.1063/5.0028816 ◽

2020 ◽

Author(s):

Clément Beust ◽

Erwin Franquet ◽

Jean-Pierre Bédécarrats ◽

Pierre Garcia ◽

Jérôme Pouvreau ◽

...

Keyword(s):

Power Plants ◽

Large Scale ◽

Storage System ◽

Steam Generation ◽

Latent Heat Storage ◽

Multi Scale ◽

Direct Steam ◽

Scale Modelling ◽

Shell And Tube ◽

Generation Power

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Electrochemical Hydrogen Production Powered by PV/CSP Hybrid Power Plants: A Modelling Approach for Cost Optimal System Design

Energies ◽

10.3390/en14123437 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3437

Author(s):

Andreas Rosenstiel ◽

Nathalie Monnerie ◽

Jürgen Dersch ◽

Martin Roeb ◽

Robert Pitz-Paal ◽

...

Keyword(s):

Hydrogen Production ◽

Power Plants ◽

Large Scale ◽

Energy System ◽

Solar Hydrogen ◽

Energy System Model ◽

Optimal System Design ◽

Solar Hydrogen Production ◽

Electrochemical Water Splitting ◽

Climate Neutrality

Global trade of green hydrogen will probably become a vital factor in reaching climate neutrality. The sunbelt of the Earth has a great potential for large-scale hydrogen production. One promising pathway to solar hydrogen is to use economically priced electricity from photovoltaics (PV) for electrochemical water splitting. However, storing electricity with batteries is still expensive and without storage only a small operating capacity of electrolyser systems can be reached. Combining PV with concentrated solar power (CSP) and thermal energy storage (TES) seems a good pathway to reach more electrolyser full load hours and thereby lower levelized costs of hydrogen (LCOH). This work introduces an energy system model for finding cost-optimal designs of such PV/CSP hybrid hydrogen production plants based on a global optimization algorithm. The model includes an operational strategy which improves the interplay between PV and CSP part, allowing also to store PV surplus electricity as heat. An exemplary study for stand-alone hydrogen production with an alkaline electrolyser (AEL) system is carried out. Three different locations with different solar resources are considered, regarding the total installed costs (TIC) to obtain realistic LCOH values. The study shows that a combination of PV and CSP is an auspicious concept for large-scale solar hydrogen production, leading to lower costs than using one of the technologies on its own. For today’s PV and CSP costs, minimum levelized costs of hydrogen of 4.04 USD/kg were determined for a plant located in Ouarzazate (Morocco). Considering the foreseen decrease in PV and CSP costs until 2030, cuts the LCOH to 3.09 USD/kg while still a combination of PV and CSP is the most economic system.

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