Reduce Operation Costs Research Articles

Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid-connected photovoltaic system

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and powerloss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of powerloss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change.

Multi-objective Bonobo optimisers of industrial low-density polyethylene reactor

Abstract A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.

Enhancing Economic Operation and Planning of Power Systems through Artificial Intelligence: Implementation of Optimal Power Flow in Chennai Utility Bus System

The aim of this paper is to integrate Artificial Intelligence (AI) into Economic Operation and Planning (EOP) methodologies of power systems, specifically by implementing an Optimal Power Flow (OPF) optimization method in the Chennai Utility Bus System. Many traditional approaches to optimize power generation and planning are inherently limited and many of these limitations can be overcome by the use of Artificial Intelligence (AI) techniques. Herein, we have used the AI powered optimization algorithms such as Multi Objective Particle Swarm Optimization (MOPSO) and Multi Objective Genetic Algorithm (MOGA) to increase the efficiency in economical ranking and also grid planning. Implementation of AI techniques in Chennai utility bus system and also evaluating it in real time using Multi Objective Particle Swarm Optimization, Multi Objective Genetic Algorithm, Newton Raphson method and their results are compared. These AI-based methods aim to reduce operation costs, minimize power loss and improve voltage stability as well as minimizing deviation of voltages in order to increase the efficiency. Future work will expand the use of these techniques to more intricate systems, such as the Indian utility 146-bus system to validate their effectiveness, in real world applications.

Improving the performance and economics of reversible solid oxide cell-based hydrogen energy storage systems through the utilization of renewable alternative fuels

Reversible solid oxide cells (RSOCs) represent a promising solution for addressing the future challenge of large-scale renewable energy storage. However, the performance and economic viability of current RSOC systems are hindered by the large overpotential losses in electrolysis and apparent fuel (H2) costs associated with power generation. This study employs a comprehensive numerical modeling approach to analyze the potential of renewable fuel (glycerol) in assisting hydrogen production performance and reducing operating costs within RSOC systems. The detailed comparative analyses and parametric studies are conducted on H2-based and glycerol-based RSOC systems at typical operating conditions. Notably, it is found that the economic viability of hydrogen-based RSOC systems is hindered by the remarkable fuel (H2) costs during nighttime despite of the favorable effects of larger stack capacity and higher temperature. While incorporating glycerol as an alternative fuel in RSOC systems offers significant advantages, including doubling the electrolytic H2 production (from 585.92 Nm3·h−1 to 1191.24 Nm3·h−1) at a relatively low cost and substantially reducing the fuel expenses (from 594.30 $·h−1 to 30.57 $·h−1) for nighttime power generation, thus leading to competitive electrolytic hydrogen production cost (2.982 $·kg−1) and noteworthy enhancement in economic viability (LCOE of 0.090 $·kWh−1 and 0.304 $·kWh−1 for electrolysis and power generation, respectively). The findings of this study provide valuable insights that contribute to the advancement of RSOC systems, thus facilitating the development of efficient, cost-effective, and sustainable energy storage and conversion technologies.

Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

ABSTRACT High costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.

Разработка СВЧ-установки для термообработки отходов убоя животных

Every day, the Russian Federation processes 126.3 tons of animal slaughter waste into animal feed. With an installation capacity of 35 kg/h, the required quantity is 7000 units. As a rule, the devices are energy-intensive. The problem is to reduce the operating costs of heat treatment of raw materials while maintaining the feed quality. The article introduces a new installation powered by an electrically driven resonator with rationalized operating modes. The research featured mucous by-products that require thermal treatment to neutralize the smell and disinfect the raw material. The authors investigated the dynamics of heating and used the data obtained to develop a digital installation model and test the electrodynamics in the resonator. After that, they rationalized the operating modes, i.e., corona discharge, electric field, screen efficiency, generator power, installation performance, and energy costs. The complex action of electromagnetic radiation provided a continuous combined heat treatment, disinfection, and odor neutralization with reduced operating costs. The electrical resonator was coaxially located in the shielding and contained a coronating electrode, electric bactericidal UV gas discharge lamps, knives, screws, and emitters from air-cooled magnetrons. The annular volume between the resonator and the coat excited the traveling wave by electromagnetic radiation through the perforation. Its average perimeter was a multiple of half the wavelength. The heat treatment with disinfection and fat rendering required an intrinsic quality factor of 8000, a generator power of 4.4 kW, a productivity of 35–40 kg/h, and energy costs of 0.25–0.28 kWh/kg. When the electric field was 5 kV/cm and the corona discharge was 9.79 kV/cm, the corona of bactericidal lamps provided the required ozone concentration and the bacterial contamination fell down to an acceptable level. The new installation with an electrical resonator reduced operating costs for heat treatment of animal slaughter waste and maintained the high-quality of the resulting feed products.

Research on Equipment Fault Identification Method and System based on Big Data Correlation Analysis

With the rapid progress in the industrial field, equipment fault identification plays a crucial role in improving production efficiency and reducing operating costs. This article proposes a specialized and efficient equipment fault identification method and system based on big data correlation analysis. This system achieves precise identification of equipment faults by finely collecting and preprocessing equipment operation data, deeply exploring potential association rules between data. The method proposed in this article not only improves the accuracy of fault recognition, but also significantly enhances recognition efficiency, providing strong support for intelligent and refined management in the industrial field.

A novel stochastic framework for optimal scheduling of smart cities as an energy hub

AbstractSmart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid‐connected hybrid AC–DC microgrid. The biogeography‐based optimization algorithm was utilized to seek the best solution for scheduling micro‐turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33‐bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown.

Data‐driven predictive control of perturbed buck converters using a modified iterative feedback tuning algorithm

AbstractThe most challenging aspect of utilizing model predictive controllers (MPCs), particularly those involving power electronic applications, is the extraction of a model that accurately represents the behavior of the studied system. Concerning the use of power electronic applications, as long as an MPC is used, adjusting the controller parameters brings difficulties. In addition, as the number of elements increases, it becomes harder to get the best control law out of the model. To do away with the need for model extraction, this study presents an offline data‐driven approach in conjunction with the MPC that can optimally adjust the MPC parameters based on the iterative feedback tuning (IFT) algorithm called the iterative feedback predictive controller (IFPC). The proposed method eliminates concerns regarding selecting an optimal number of algorithm iterations, thereby reducing operating costs, by introducing a modified IFT called feedback‐based IFPC (FIFPC) while simultaneously achieving optimal MPC parameters. The proposed method is applied to a constant voltage load (CVL) connected less‐than‐ideal buck converter, that is, one with perturbed filter elements and variable loads. A robust stability analysis (RSA) is performed under normal operating conditions to investigate the robustness behavior of the proposed controller. Simulation studies are presented to evaluate the proposed controller under different scenarios, such as step and abrupt load changes and measurement noise, compared with the well‐known model‐based and data‐enabled predictive controller (DeePC) approaches in the MATLAB/Simulink environment.

A crowdsourcing framework for green urban parcel delivery: Utilizing private cars that will be in passing

The strong increase of urban parcel delivery creates significant pressure on delivery enterprises. Nowadays, delivery enterprises do not only reduce operating costs, but also undertake a social responsibility: to reduce environmental costs caused by carbon emissions. This paper investigates the crowdsourcing framework to deliver urban parcels greenly by utilizing private cars that will be in passing. The bonus incentive strategy is introduced to improve the delivery willingness of private car drivers. Aiming at minimizing the sum of environmental costs caused by carbon emissions and enterprise costs, we establish a mixed-integer programming (MIP). To solve the problem, a hybrid genetic algorithm (GA) that combines adaptive large neighbourhood search (ALNS) and GA is proposed. The proposed approach is verified by standard instances. The results show that the green crowdsourcing framework with the participation of private cars will reduce the total costs. This approach has a great potential of reducing urban traffic growth and environmental pollution. Based on the real-word network in Dalian, the influences of the number of private cars, bonus incentive coefficient and time slack coefficient on the environmental costs and enterprise costs were investigated. Our research conclusion can provide a more reasonable decision-making basis of green urban parcel delivery for delivery enterprises.

Natural pyrite activated peroxydisulfate enhanced short-chain fatty acids production from anaerobic fermentation of waste activated sludge: Performance and mechanisms

Peroxydisulfate (PDS) additive has been proven as an effective strategy to enhance short-chain fatty acids (SCFAs) production from waste activated sludge (WAS), which can achieve dual objectives of waste disposal and resource recovery. However, the high cost of PDS limits its large-scale application in sludge treatment. This study explored the feasibility of using natural pyrite (FeS2) as an activator for PDS to enhance its capacity for promoting SCFAs production while reducing the required dosage. Compared to sole PDS (1 mM/g volatile suspended solids (VSS)), FeS2/PDS co-treatment (0.5 mM/g VSS PDS + 0.6 mM/g VSS FeS2) obviously increased the maximum SCFAs production from 4589.5 mg COD/L to 5951.3 mg COD/L, accompanied with an enhanced proportion of acetate from 62.0 % to 75.2 %. Mechanism exploration revealed that FeS2/PDS enhanced the generation of hydroxyl radicals (•OH) and sulfate radicals (SO4•−), thereby promoting WAS solubilization and hydrolysis. The detectable soluble chemical oxygen demand within the supernatant after 4 days of co-treatment fermentation was 5.3-fold higher than that in the single FeS2 test and 2.0-fold higher than that in the single PDS test. Meanwhile, the activities of key enzymes, including protease, α-glucosidase, acetate kinase, and butyrate kinase, were also significantly enhanced after the FeS2/PDS treatment, accompanied with an increase in the abundance of hydrolytic and acidogenic microorganisms such as Acinetobacter, Clostridium_sensu_stricto, and Fonticella. Besides, FeS2/PDS co-treatment improved the dewaterability (42.7 % reduction in capillary suction time) and reduced operation cost (37.9 % decrease), as compared to the sole PDS (1 mM/g VSS) treatment. These findings suggest that FeS2/PDS co-treatment is a promising and cost-effective technique to enhance WAS anaerobic fermentation.

Process optimization and economic evaluation of continuous multi-column capture for monoclonal antibody – A case study

In recent years, due to significant improvements achieved in upstream processing of monoclonal antibody (mAb) manufacturing, the focus on productivity and cost-effectiveness has shifted towards downstream processing. Protein A affinity chromatography has been widely employed as the capture step for mAb purification and serves as a key driver of economic efficiency on downstream operations. To enhance the purification process and alleviate manufacturing constraints while reducing costs, continuous processing, such as multi-column capture (MCC), has gained attention. This study aimed to establish and optimize the protein A MCC process and to evaluate it with traditional single-column capture mode. Through a series of verification runs, it was determined that there was no significant difference in yield and quality attributes across different loops. Furthermore, annual cost of goods (COGs) was evaluated with the conventional single-column batch process. The results showed that MCC process reduced operation cost by 18 % and almost doubled productivity in the specific scenario.

Earning Moderates the Influence of CAR, NPL, and BOPO on Company Value in Conventional Banks Listed on the IDX for the 2020-2022 Period

The goal of research on traditional commercial banks listed on the Indonesia Stock Exchange (IDX) is to use earnings as a moderator to ascertain the impact of the capital adequacy ratio (CAR), non-performing loan (NPL), and BOPO on the value of the company. Variables This study uses a quantitative approach and sample data collection through purposive sampling. The samples obtained for this study are 69 samples that meet the standards of the Indonesia Stock Exchange (IDX) and financial statements for the 2020-2022 period. Classical hypothesis testing and moderate regression analysis (MRA) methods were used to analyze the results of this study. The results showed that the Capital Adequacy Ratio (CAR) did not have a significant effect on the value of the company, and non-performing loans (NPL) did not have a significant effect on the value of the company and operating costs. (BOPO) does not have a significant influence on the value of the company. Capital Adequacy Ratio (CAR) can be changed and affect company value, profit cannot moderate the impact of non-performing loans (NPL) on company performance Product value cannot reduce Operating Costs and Operating Income (BOPO) to company value.

CURRENT PROBLEMS IN THE COMBUSTIBLE MUNICIPAL WASTE MANAGEMENT – A CASE STUDY FOR PRE-RDF, RDF AND SRF MANAGEMENT IN POLAND

Refuse-derived fuel (RDF) is one of the municipal waste processing products generated inmechanical-biological installations. The RDF production allows energy recovery while reducingthe mass of landfilled waste. RDF is produced mainly from mixed waste stream with combustibleproperties. Currently, the main recipient of RDF fuels in Poland is the cement industry, whichaccepts only high-quality fuels. Annually a reduction of combustible waste share in the pre-RDFfraction intensifies difficulties in producing fuels with a sufficiently high calorific value. However,due to the energy transformation, it is planned that RDF would also be used in the heating sector.A positive aspect of this situation is the emergence of alternative recipients of RDF, even with theirlower calorific value. This may improve the market situation of RDF and reduce operation costs formunicipal installations in the long-term perspective.

Benders Decomposition-based Coordinated Optimization Method for Medium and Low Voltage Distribution Network with Photovoltaics Access

aims: From the perspective of improving the economic benefit of the distribution network and ensuring the safe operation of the distribution network, the medium and low voltage optimization model with the goal of reducing the operation cost is established. background: Double carbon background and new energy power system policy, a large number of distributed photovoltaic access distribution network caused a great influence, not only on the low voltage distribution network user side power quality impact, photovoltaic full hair also can happen when tide back phenomenon, for the stable operation of the medium voltage distribution network and scheduling, therefore, this paper puts forward a scheduling of the voltage distribution network and low voltage distribution network optimization method, to solve the problems of large-scale distributed photovoltaic access. objective: Reducing operating costs and ensuring the stable and safe operation of the distributed network. method: The optimization problem of medium and low voltage distribution network is decomposed into the main optimization problems of medium voltage distribution network and the sub-problems of optimizing the low-voltage distribution network. An optimal solution is obtained by coordinating the boundary coupling variables between the two problems and solving them iteratively. Finally, the effectiveness of the coordination algorithm is verified by simulating the medium and low voltage distribution network. result: A coordinated optimization method for medium and low voltage distribution network with photovoltaics access. conclusion: It reduces the operation cost, ensures the stable and safe operation of the power grid, thus improving the economic benefits. other: None

Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks.

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.

Digital Transformation in Nautical Tourism

Digitization in the domain of nautical tourism has stimulated the implementation of innovative applications, serving as integral support mechanisms for service users. This transformation has also given rise to specified smart business ecosystems, ensuring the joint generation of value for all stake-holders. As a result, innovative technologies have not only transformed the overall nautical experience but also led to reduced operating costs and the development of effective business practices, thus becoming a strategic priority for nautical companies. Based on the analysis of a wide body of literature, this paper aims to underscore the significance of information technology and interactive smart systems in nautical tourism. Through the implementation of digital and smart technologies, it becomes feasible to manage multiple segments, including actors, devices and security systems. This operationalization of ship management activities aims to enhance the experience for guests and crew in continuation.

Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and multiple energy storage systems

To achieve efficient energy management in complex integrated energy systems (IESs) with renewable energy sources (RESs) and multiple energy storage systems (ESSs), the study aims to propose a novel approach. Evolutionary-based methods are difficult to find the optimal scheme, while deep reinforcement learning (DRL)-based methods face problems with low training efficiency. To address the limitations, a knowledge-network-embedded DRL method is proposed, which combines the fast-searching capability of genetic algorithm (GA) with the environment-aware decision-making capability of DRL, with the aim of developing a highly efficient energy management strategy in improving user comfort and reducing operating costs. GA is used to solve the IES optimization model for obtaining a solution set that serves as domain knowledge, and a knowledge network is constructed using deep learning (DL). Then, DRL uses this network as an actor network to explore the optimal strategy. The use of GA helps the DRL to enhance training efficiency and find an optimal strategy. Simulations show significant improvements in cost reduction (5.7 %) and user comfort (4.2 %), along with improved training efficiency (58.3 %) compared to baseline DRLs.

Supported bimetallic PtNi-vinyl complex for hydrosilylation of straight-chain terminal alkenes with tertiary silanes: Effect of Ni promoter on catalytic performance

A bimetallic PtNi-vinyl complex as catalytic active center was created on modified rice straw biochar by introducing an appropriate amount of cheap Ni component as a catalytic promoter. The constructed bimetallic catalyst (PtNi-VTES-RSOC) was used to catalyze hydrosilylation of terminal alkenes with tertiary silanes to produce alkylsilanes with high commercial application value. The experimental results proved that the conversion of 1-octene 96.5 % and selectivity for target product 97.1 % were obtained in hydrosilylation of 1-octene with triethoxysilane catalyzed by PtNi-VTES-RSOC with mass ratio of Pt/Ni = 1.5:1 (molar ratio of Pt/Ni≈1:2) under mild conditions (atmospheric pressure, solvent-free, 50 ℃ and 3 h). It also possessed higher catalytic ability and regioselectivity for hydrosilyation of long-chain alkenes (1-octadecene) with tertiary silanes compared to the counterpart monometallic catalysts. The characterization and theoretical calculation results demonstrated that the introduced suitable amount of Ni(II) could affect the structure of original Pt(II)-vinyl complexes and form a stable and unique bimetallic vinyl coordination microstructure by using a shared vinyl group, thereby affecting morphology, structure, and catalytic performance of the as-prepared bimetallic catalyst with mass ratio of Pt/Ni = 1.5:1. Changing the introduction order and introducing excessive or insufficient of Ni(II) would both disrupt the formation of this special structure and affect the catalytic performance. Additionally, the formed stable complex structure with vinyl groups could well maintain the catalytic stability. The study results exposed that the as-prepared bimetallic catalyst would possess a great industrial application potential in the hydrosilylation of alkenes by efficiently increasing the product quality and reducing operation cost.

Air leaks fault detection in maintenance using machine learning

PurposeThe objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.Design/methodology/approachEffective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.FindingsBased on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.Practical implicationsEarly air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.Originality/valueTo the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.