Compressed Air System Research Articles

Improving the efficiency of energy saving through the use of thermal energy

The article analyzes the problem of energy saving in the context of organizational and technical measures. One of the energy saving factors is the use of waste heat of various potential. The advantages of using waste heat in the energy sector and the scope of their application are considered. Power plants for waste processing and their parameters are described. Examples of the creation of special energy devices for the chemical industry are given. The connection of internal (by-product) energy resources (IER) with technologies using melting furnaces is noted. The boundaries of the use of heat recovery boilers (HRB) in various industries are determined. The schemes of HRB application at the wastewater disposal site with quantitative and qualitative characteristics are given. A method for improving economizers using bimetallic pipes and heat exchangers is given. The use of batteries in the disposal and consumption of waste heat is justified. The ways of increasing the efficiency of compressed air systems and the features of using arc steelmaking furnaces to reduce energy consumption are considered. The comparison of local heat supply schemes with regenerative ones is carried out and the prospects for the development of these schemes are noted.

АНАЛІЗ ВПЛИВУ ТЕМПЕРАТУРИ ЗОВНІШНЬОЇ СЕРЕДИ НА РОБОТУ ФОТОЕЛЕКТРИЧНИХ СТАНЦІЙ

This work is devoted to the study of the mechanism of increasing the efficiency of solar photovoltaic generation by integrating the compressed air system. The main aspects of this mechanism, its advantages and influence on the improvement of the process of converting solar energy into electrical energy are considered. The relevance of this topic is due to the constant growth of electricity consumption and the need to create efficient and sustainable solar power plants. Solar photovoltaic generation is already a promising source of renewable energy today, but it has certain limitations, in particular, related to the increase in temperature of solar panels and uneven distribution of solar radiation. This paper examines the integration of a compressed air system with solar panels to increase the efficiency of solar PV generation. The use of compressed air allows you to lower the operating temperature of the panels, which leads to an increase in the efficiency of solar energy conversion. In addition, it helps to unify the lighting of the panels and improves the performance of all photovoltaic modules. An important aspect of the work is the analysis of the influence of the integrated compressed air system on the energy characteristics of solar panels. The study includes modeling and experimental determination of changes in efficiency, energy output, and stability of solar panels when using a compressed air system. The obtained results allow us to draw conclusions about the efficiency and practical implementation of an integrated system of compressed air for solar photovoltaic generation. The identified advantages of this approach make it possible to recommend it for use in modern solar power plants in order to increase efficiency and ensure stable operation.

Condition Monitoring and Fault Diagnosis of a Marine Diesel Engine with Machine Learning Techniques

A marine engine room is a complex system in which many different subsystems are interacting with each other. At the center of this system is the main diesel engine which produces the propulsion force. Many other components such as compressed air, cooling, heating, lubricating oil, fuel, and pumping systems act as auxiliary machines to the main engine. Automation of many functions in the engine room is starting to play an important role in new generation ships to provide better control using sensors monitoring the engine and its environment. Sensors exist in the current generation ships, but engineers evaluate the sensor data for the presence of any problems. Maintenance actions are taken based on these manual analyses or regular maintenance is carried out at times determined by manufacturers, whether such actions are needed or not. With machine learning, it is possible to develop an algorithm using past evaluations made by engineers. Recent studies show that highly accurate results can be obtained using machine learning methods when there is sufficient data. In this study, we develop new learning-based algorithms and evaluate them on data obtained from a realistic ship engine room simulator. Data for a predetermined set of parameters of a high-power diesel engine were collected and analyzed for their role in a set of fault situations. These fault conditions and the associated sensor data are used to train a set of classifiers achieving fault detection up to 99% accuracy. These are promising results in preventing future damage to the engine or its supporting components by predicting failures before they occur.

Utilizing machine learning models to estimate energy savings from an industrial energy system

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.

Review of Compressed Air Receiver Tanks for Improved Energy Efficiency of Various Pneumatic Systems

This review examines compressed air receiver tanks (CARTs) for the improved energy efficiency of various pneumatic systems such as compressed air systems (CAS), compressed air energy storage systems (CAESs), pneumatic propulsion systems (PPSs), pneumatic drive systems (PDSs), pneumatic servo drives (PSDs), pneumatic brake systems (PBSs), and compressed air vehicles (CAVs). The basic formulas and energy efficiency indicators used in a CART calculation and selection are included. New scientific research by the authors on measurements based on tank methods, numerical solutions in the process of charging and discharging, the valve-to-tank-to-valve system and pneumatic propulsion system was presented. The numerical model of the valve-tank-valve system takes into account CART polytropic charging and discharging processes, the mass flow balance equation, and the sound (choked) and subsonic mass flow rate in the inlet and outlet valves. Future research directions to improve the energy efficiency of a CART charging and discharge are highlighted. The effective density of energy storage in CART was compared to that of other renewable energy sources and other fuels. Economic and environmental issues were also considered by adopting various energy performance indicators. The discussion also focused on the design concept and computational model of the hybrid tricycle bike (HTB) pneumatic propulsion system.

Hierarchical reinforcement learning based operational optimization for compressed air system

The high proportion energy consumption of compressed air system (CAS) have drawn more and more attentions, and its energy conservation and emission reduction is of great significance to achieve the green transformation in an industrial park. In this study, a novel operational optimization method for CAS by hierarchical reinforcement learning (HRL) strategy is proposed, which involves two modules in terms of the operational optimization by combining the selectable of start–stop devices and operational mode set, the control optimization by adaptive parameter controller. Moreover, the close interaction between operational optimization and tracking control by using the hierarchical structure with Q-learning process. Besides, a feedback correction strategy is also proposed for reducing the negative effect of prediction output error and compressor physical limitation. Furthermore, a series of typical cases in an industrial park are selected for verifying the effectiveness of the proposed HRL, which shows that the proposed method achieves the best trade-off between complexity and time consuming.

Reliability analysis of the compressed air supplying system in underground mines

Despite the high cost and low efficiency, compressed air is mostly used in underground mining for ore extraction, hoisting, and mineral processing operations. Failures of compressed air systems not only threaten the health and safety of workers but also contribute to inefficient control of airflow and stopped all equipment that operates by compressed air. In such uncertain conditions, mine managers are faced with the big challenge to supply enough compressed air, and therefore, the reliability evaluation of these systems is essential. This paper aims to analyze the reliability of the compressed air system using the Markov modeling approach as a case study, Qaleh-Zari Copper Mine, Iran. To achieve this, the state space diagram was constructed considering all relevant states for all compressors in the main compressor house of the mine. The failure and repair rate of all main and reserve compressors were calculated for all possible transitions between states to obtain the probability of being of the system in each of the states. Moreover, the probability of failure at any time period was considered to study the reliability behavior. The results of this study show that there is 31.5% probability that the compressed air supplying system is in operating condition with two main and one standby compressors. The system probability that two main compressors are remain in the operation without failure for one months is 92.32%. Furthermore, the lifetime of the system is estimated 33 months when at least one main compressor is active.

Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review

The industrial sector is a crucial economic pillar, seeing annual increases in the production output. In the last few years, a greater emphasis has been placed on the efficient and sustainable use of resources within industry. The use of compressed air in this field is hence gaining interest. These systems have numerous benefits, such as relative low investment costs and reliability; however, they suffer from low-energy efficiency and are highly susceptible to faults. Conventional detection systems, such as ultrasonic leak detection, can be used to identify faults. However, these methods are time consuming, meaning that leakages are often left unattended, contributing to additional energy wastage. Studies published in this area often focus on the supply side rather than the demand side of pneumatic systems. This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side of compressed air systems, leading towards a comprehensive understanding of smart and sustainable pneumatic systems. Fifty-three studies were classified and reviewed under the following three areas: (a) demand parameters which help in identifying fault sources; (b) approaches taken to analyse the parametric data; and (c) the role of Artificial Intelligence (AI) in pneumatic fault monitoring systems. This review shows that fault detection on the demand side has received greater importance in the last five years and that data analysis is crucial for AI to be implemented correctly. Nevertheless, it is clear that further research in this sector is essential, in order to investigate more complex systems. It is envisaged that this study can promote the adoption of such systems, contributing to an energy-efficient and cost-effective industry.

Research on Typical Pipeline Design Method of Power Transmission Auxiliary System based on Vibration Control

The power transmission auxiliary system is a set of key systems of armored vehicles, which roughly includes a heat dissipation system, a lubrication system, a compressed air system, a heating system, a thermal smoke screen system, and other subsystems. It is characterized by relying on a pipeline connection so that the auxiliary system and the engine, and the transmission system form a closed loop to achieve functional output. The pipeline of the auxiliary system is mainly arranged around the engine, and the main excitation source is the engine. To improve the reliability of the power transmission auxiliary system of armored vehicles and avoid the phenomenon of running, running, dripping, and leaking, this paper mainly proposes a design method based on vibration control for the typical pipelines commonly used in armored vehicles and determines the excitation frequency of the pipelines mainly at 0~500Hz through testing the excitation source, of which the vibration energy of 150Hz~400Hz is the largest and then attenuated with the analysis of pipeline modalities by hammering test method and finite element method. By simulating the pipeline modality, and analyzing its 1st to 6th-order modes, the pipeline is optimized by changing the pipe diameter, changing the bending radius, increasing the pipeline branch, increasing the flexible link, increasing the constraint, etc., so that the pipeline avoids the resonance frequency in the corresponding mode to obtain the best design scheme of the pipeline. The research method in this paper has been successfully applied to the structural design of the test of the armored vehicle auxiliary system pipeline and other model projects. An effective design process has been formed, and the typical pipeline designed by the method has been completed with prototype trial production, bench test, and real vehicle test. The pipeline work performance is good, and the operation is smooth and reliable.

Isobaric storage of compressed air: Introduction of a novel concept based on phase change materials and pressure equalizing modules

In industrial compressed air systems (CAS), compressed air receivers have a significant impact on the overall energy efficiency. Isobaric compressed air receivers have the potential to increase the amount of stored air significantly and, hence, the energy efficiency can be improved compared to the usage of conventional isochoric compressed air receivers. In this work, we propose a novel concept of an isobaric compressed air receiver based on pressure equalizing modules (PEQM). These PEQM are filled with a condensable gas – in this work called phase change material (PCMl/g) – and are placed inside the receiver. During the charging and discharging of a compressed air receiver equipped with PEQM, the PCMl/g condenses or evaporates, respectively. Due to the large volume change during the phase change of the PCMl/g air can be stored isobarically. However, a proper selection of suitable PCMl/g is crucial for efficient and safe use. Therefore, we introduce a three-step selection process based on an adapted digital-logic method. In this selection process, qualitative (e.g. flammability) and quantitative (e.g., heat of condensation) properties are considered. As a result, R-1234ze(E) is selected as PCMl/g which owns a vapor pressure of 800 kPa at a temperature of 314.7 K. First experiments with the novel PEQM filled with R-1234ze(E) have been conducted in a lab-scale test plant and the results are shown in this work. The storage capacity of the test plant has been increased by 37.9 %. Additionally, the phase change of the PCMl/g during charging and discharging has been observed. However, the phase change is not isobaric yet. Temperature changes during the phase change inside the PEQM hinder the isobaric storing of the working gas. Therefore, proper heat management inside the PEQM is necessary, for example, by using a second phase change material for heat storage.

Shipboard compressor system risk analysis by using rule-based fuzzy FMEA for preventing major marine accidents

Considering the shipboard machinery, the compressed air system directly powers the ship's diesel engines' starting and routine operations. Additionally, pressurized air is distributed throughout the ship, enabling the operation of various systems. However, a compressor failure could result in the entire compressed air system collapsing, which might also result in the ship losing its maneuverability. When a ship loses its ability to maneuver, various devastating risks can arise, including grounding, collision, contact, explosion, and fire. On the other hand, compressor system risk assessment is not a widely researched subject in the field of maritime. To fill this gap, this study provides a comprehensive risk assessment of the shipboard compressor system by utilizing the rule-based fuzzy Failure Mode and Effects Analysis (FMEA) method. 33 failure modes with their causes and consequences were determined with their expert-assigned O–S-D scores. In addition, 125 If-Then rules in the fuzzy logic system were created specifically for this study. Finally, the failure modes of the shipboard compressor system were prioritized. According to the results, the most critical failure modes are identified as FM18-Broken piston rings with a 6.43 fuzzy RPN score, FM08-Piston liner wear with 6.29, and FM11-Knocking compressor operation with a 5.59. The results of this study will contribute to the maritime sector by analyzing the risks of compressor systems and introduce them to safety specialists, ship crew, shipping companies, port authorities, inspection officers, etc.

Experimental analysis of one micro-compressed air energy storage-power generation system with different working fluids

Since the industrial revolution, coal, oil, and natural gas have been burned to emit additional carbon dioxide into the atmosphere. Renewable energy should therefore be widely used, from the current 26 % to 86 % by 2050. However, solar energy and wind energy have problems such as intermittent and instability. This paper uses compressed air for energy storage. The results show that the generated power capacity increases as the inlet gas pressure of the turbine increases, while the total efficiency decreases with the inlet gas pressure. In addition, the highest power generation capacity is not at the highest total efficiency point. The highest total efficiency is about 0.519 and the generated power capacity can reach the peak value of 663.6 W in the compressed air system.

Energy Utilization and Saving Opportunities in Process Industries: Case Study of Textile Manufacturing Industry in Kenya

The industrial sector is seriously threatened by high energy costs brought on by inefficient energy use. Energy, together with labor and raw materials, make up the majority of production costs in the textile industry. As a result, the only real way for the textile industry to turn a profit and maintain its competitiveness is to reduce its energy costs through energy conservation measures and energy management strategies. The main objective of the study was to investigate energy utilization in textile manufacturing plant and identify energy saving and conservation measures that help reduce energy costs. In the current work, energy investigation was carried out to determine performance of high energy consuming utility systems and equipment such as compressed air systems, boilers, motors, lighting systems and steam distribution systems. Boiler flue gas analysis test, air compressor air leak test, heat loss analysis for steam distribution system, motor load assessment and load variation trends in air compressor were done. Energy utilization analysis showed that high electricity consumption occurs in spinning (48%), followed by weaving (26%) and wet processing (22%). Statistical analysis of energy consumption in spinning, weaving and wet processing showed linear relationship between production and energy consumption with correlation coefficients (R2) of 0.782, 0.708 and 0.637 respectively which reveals that there continues to be a large potential of energy efficiency across all the major textile manufacturing processes in the plant. The study found potential savings in electric energy to be approximately 367,784 kWh/year, wood fuel savings 1,219 tones/year and furnace oil savings to be 22,275 L/year. The results displayed 31.6% overall energy cost saving potential with an average simple payback period of 0.5 years on the investigated high energy consuming utility systems and equipment. In conclusion, significant energy savings in the sector might be attained by implementing free or inexpensive investments including sealing air and steam leaks, insulating bare pipes, and properly drying wood fuel.

Investigation of Technical Feasibility of Hybrid Compressed Air Cogeneration Systems under Energy-Flexible Operation

The study investigates the effects of energy-flexible operation of hybrid compressed air systems in production processes with a view to efficiency and energy consumption. As energy costs become increasingly important in the economic considerations of industrial companies, integrating renewable energies necessitates the implementation of demand-side management capabilities to overcome the problem of intermittent energy production. One promising solution is hybridization, enabling energy systems to utilize multiple forms of energy. In the context of hybrid compressed air systems (CAS), it enables the use of gas, for example natural gas, hydrogen gas or biogas, and electricity for compressed air production, thus substituting electricity or gas demand on short- and long-term horizons. Advantages of such systems include the provision of energy flexibility as well as the variable supply of compressed air, process heat, and electricity through recuperation in the electric motor according to production plant consumption. However, the effects of energy-flexible operation on energy consumption and thus on the system's efficiency still needs to be fully understood. To this end, a demonstrator of a hybrid CAS cogeneration plant was built. Specific scenarios are used to analyze the effect on energy efficiency and the feasibility of energy-flexible operation. Despite slight reductions in efficiency during operation, the feasibility of operation was validated and valuable insights on further improvement were gained. With a view to the expected rising volatility in energy production, the hybrid approach represents an efficient, climate-friendly, energy-flexible, and resilient solution for compressed air production that could assist in expanding industrial demand-side management.

Development of the algorithm for implementation of energy-efficient compressed air systems with energy recovery

In order to improve the energy efficiency of pneumatic systems, this paper presents an algorithm for the development and implementation of an energy efficient pneumatic control system with energy recovery of compressed air. Two different ways of forming closed pneumatic circuits that reuse already used compressed air are presented. Compared to traditional pneumatic control, significant energy savings are achieved.

Indoor Air Quality Management under Button Up Conditions

Abstract: Maintaining the desired Indoor Air Quality under the Button Up Conditions, has always been a challanging task. The present invention provides Indoor Air Quality Management System Under Button Up Conditions[1]. The system is operated in a Normal, Button Up or Filtration mode. The invention comprises computer based fully automated system which include six sub systems i.e., CO2 Removal System, Odour/ TVOC Removal System, Oxygen Replenishment System, Compressed Air System, NBC Filtration System and Facility Management System that operate in coordination with one another to maintain the desired CO2, Oxygen and Positive Pressure levels in facility. CO2 adsorbents which adsorb CO2 from the air by way of not only their chemical reactions but also or only by way of their molecular structure and other properties. Activation of CO2 Removal System is based on the signal from the CO2 sensor that is located in the return air duct of the air handling units serving the buildings. The CO2 Removal System does not depend only on one type of absorbent/adsorbent material. All the six sub systems are fully integrated with one another to always maintain the desired levels of CO2, Oxygen and positive pressure in the facility and filter away any hazardous contaminants from the air under all three modes of operation i.e., Normal Mode, Button Up Mode or Filtration Mode as felt necessary. The Bunkerman absorbents used in the system, showed CO2 absorbent capacity of about 35% to 42% by weight. The adsorbent capacity of the Bunkerman Adsorbents and molecular sieves were observed to be between 15 to 18. An absorbent capacity of 30% and adsorbent capacity of 12% can be safely adopted for CO2 removal filters in the systems. The proposed Bunkerman Indoor Air Quality Management System was effectively able to ensure the recommended levels of CO2 and TVOC [2,3] in the facility tested during Real Button Up Mode.

Perawatan Kompresor Udara Guna Menunjang Olah Gerak Kapal MV. CHARLOTTE

The purpose of this study is to detail the main causative factors of reservoir water tank issues that lead to delays in operating the main engine. The data analysis method used is qualitative descriptive analysis. The results show that the reasons for the compressor malfunction during the maneuvering of the MV. Charlotte include broken piston rings, damaged compressor cylinder head gaskets, leaks in high and low-pressure valves, and compression leaks in the compressor. The solution involves shutting down the compressor, closing the intake/outlet valves, removing the connecting rod bolts, replacing the piston rings, conducting a running test, cleaning the cylinder head surface, applying liquid gasket, and ensuring no leakage after installation. It is important to check and ensure the cylinder head bolts are tightened according to the manufacturer's manual. Additionally, shutting down the compressor, opening the valves, and performing skimming or replacing damaged components are necessary. Energy savings can be achieved by improving the compressed air system, which can reduce operational and maintenance costs. Compressor efficiency can be enhanced by using automatic controls to regulate compressor operation as needed and maintaining the system's air pressure as low as possible to minimize leakage. The conclusion emphasizes that efforts to ensure the compressor functions properly during ship maneuvers include repairing or replacing compressor components when damage is identified. If repairs are not possible, component replacement must be carried out immediately without delay.

Efficiency Analysis of an Arrayed Liquid Piston Isothermal Air Compression System for Compressed Air Energy Storage

Efficiency Analysis of an Arrayed Liquid Piston Isothermal Air Compression System for Compressed Air Energy Storage

Optimization-based economic analysis of energy storage technologies in a coupled electricity and natural gas market

This work presents a stochastic mixed-integer linear programming (MILP) optimization framework to investigate the optimal participation and economics of various energy storage technologies, such as pumped-hydro, advanced adiabatic and diabatic compressed air systems and li-ion battery, in a perfectly competitive coupled electricity and natural gas market. The clearing scheme pertains to energy-only markets, aiming to optimize dispatch and maximize social welfare for the integrated energy system, through a two-stage stochastic programming. The first stage presents the day-ahead market clearing procedure, while the second stage illustrates the integrated system operation in the real-time trading floor, through a set of plausible wind power generation realizations. The two markets mainly interact the bilateral operation of the diabatic compressed air energy storage system, both as an electricity producer and as natural gas consumer. The proposed algorithm is applied to a modified IEEE 24-bus power grid and a single-node gas network and provides a thorough analysis of the operational characteristics and profitability of each energy storage technology in the integrated energy system. Results illustrate that electricity storage systems can increase their overall profits under power transmission congestion and while wind power generation volatility increases from 5 % up to 52 %. In contrast, a decrease by 3.1 % in the social welfare is realized in the case of power congestion while this figure is approximately 2.54 % under wind power volatility increase.

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.