Intelligent lift solutions for energy efficiency

The oil and gas industry plays a pivotal role in global energy production, yet it faces challenges related to energy consumption and efficiency. The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors, including oil and gas, by enhancing operational efficiency and sustainability. This review explores the impact and future directions of IoT in improving energy efficiency across the oil and gas industry. IoT devices enable real-time monitoring and control of equipment and processes, facilitating data-driven decision-making and optimization of energy usage. In the context of oil and gas operations, IoT sensors deployed throughout the production, refining, and distribution processes can gather vast amounts of data on parameters such as temperature, pressure, flow rates, and equipment performance. Analyzing this data using advanced analytics and machine learning algorithms allows for predictive maintenance, reducing downtime and optimizing energy consumption. Furthermore, IoT-driven solutions enable remote monitoring and management of facilities, minimizing the need for onsite personnel and streamlining operations. By integrating IoT technologies with existing infrastructure, oil and gas companies can enhance energy efficiency, reduce operational costs, and mitigate environmental impacts. Looking ahead, the future of IoT in the oil and gas industry lies in further advancements in sensor technology, data analytics, and connectivity. Continued investment in research and development is essential to unlock the full potential of IoT for improving energy efficiency and sustainability in this critical sector. Collaboration among industry stakeholders, technology providers, and policymakers will be crucial in driving innovation and implementing IoT solutions at scale. Embracing IoT promises to revolutionize the oil and gas industry, paving the way for a more efficient, resilient, and environmentally friendly energy landscape. Keywords: Oil and Gas, Energy, Efficiency, IoT, Industry, Review.

Synchronization of a large-scale lifting system with hydraulic actuator failures is investigated in this article. The lifting system is composed of multiple intelligent lifting subsystems with hydraulic actuators, wireless data transfer unit, and distributed controller. During the lifting process, the hydraulic actuators are possible to be malfunctioned. Once actuator failure occurs, the number of lifting points and the communication topology would change over different time intervals. This article proposes a distributed synchronization control method and adopts switching technique in analyzing the lifting synchronization. The distributed controller is designed with information received from around subsystems through wireless data transfer unit rather than with direct reference signal from the control station. On the basis of Lyapunov stability theory and switched technique, sufficient conditions that guarantee the synchronization of the lifting system with actuator failures are achieved, and synchronization errors can be reduced as small as desired. Finally, the effectiveness of proposed distributed synchronization controller is verified by numerical simulations conducted on AMESim platform. From the simulation results, it can be seen that when actuator failures occur, the synchronization error of the remaining lifting subsystems is less than 5%. The lifting synchronization error shrinks to 5% in 5.87 s when a broke-down subsystem returns to normal.

To efficiently reduce the development and production costs of the intelligent lifting control system, we introduce a design method for the intelligent lifting system with client-server architecture. We replace DSP processor core or DSP (Digital Signal processor) core with Nois II soft-core processor so that the design and production costs can be effectively cut. By replacing DSP processor or DSP processor core with Nois II soft-core processor, the design and production costs can be significantly reduced. In our design, loop vector control units work as a server processor, and a central computing unit with four independent multipliers and two adders is employed, with the implementation method based on a state machine. The experimental results prove effective in reducing resource requirements for FPGA (Field Programmable Gate Array), show that the proposed method can be successfully applied to the implementation of a complete intelligent flexible lifting control system on a low-end Altera Cyclone FPGA, and servo motor control achieves better dynamic performance

Chapter 17 - Empowering a sustainable future: unleashing the potential of machine learning for energy efficiency and conservation

This chapter explores the role of Artificial Intelligence (AI) in enhancing energy efficiency and sustainability in smart warehousing. AI technologies such as predictive maintenance, smart inventory management, dynamic load balancing, and automation have been shown to significantly reduce energy consumption, optimize warehouse operations, and minimize environmental impacts. AI's integration with renewable energy sources, like solar and wind, further supports sustainability goals by optimizing energy usage and reducing reliance on non-renewable power. The future potential of AI in revolutionizing warehouse energy efficiency is immense, with advancements in deep learning, edge computing, and real-time analytics. This chapter also highlights areas for future research, particularly in renewable energy integration, AI algorithm development, and scalability for small and medium-sized enterprises.

In order to solve the needs of a large number of users and the efficiency of user service requirements, an optimization strategy analysis method of intelligent product service system based on computer simulation technology is proposed. Aiming at the two challenges of massive requests and efficient service in the network intelligent service system, the characteristics of the network intelligent service system are analyzed, and the network intelligent service system is formally modeled as an agent system. The performance modeling, performance evaluation, and performance optimization methods of the network intelligent service system are proposed. In order to solve this massive demand, the performance modeling of the network intelligent service system is carried out based on queuing theory. This paper proposes a task assignment algorithm for the network intelligent service system. Through the application of these results, the processing efficiency of the entire medical insurance application system has been greatly improved, and the average completion time of all businesses has been shortened by 59%, providing new ideas for optimizing service methods and improving service quality.

Summary An effective and accurate downhole communication strategy is crucial for the fabrication of an intelligent lifting system for onshore oil wells. Traditional communication approaches based on the wired cable, acoustic wave, vibration wave, or fluid pressure are usually limited by downhole conditions, and issues such as cumbersome implementation, limited communication, and unstable signal modulation are encountered. Herein, a novel downhole communication strategy is proposed using the loading waves in the sucker-rod pumping system (SRPS). The loading wave is altered at the downhole pump at an extremely low frequency, and its significant variation could be captured by the surface load sensor. A controlled valve is installed between the chamber of the pump and the wellbore. The valve opening regulates the pressure in the pump chamber, leading to the generation of the controlled loading waves. The field tests are further carried out and prove the effective coding between the downhole and surface with an acceptable delay (~0.154 seconds for a well with a depth of 1000 m). For the loading wave transmission on the sucker-rod string system, the finite element method is used to solve the theoretical model considering the real circumstances, such as the coupling damping, centering device friction, and stuffing box friction. The impacts of operating parameters of the lifting system, wellbore conditions, and modulation of excitation signal on the communication process are systematically discussed. The transmission evaluation standard, applicable conditions, coding tactic, and potential engineering values are presented for the downhole communication system.

Future communication paradigms, such as 6G networks, emphasize self-sustainability, intelligent networking, and secure, adaptive communication. This research presents an innovative routing framework tailored for Underwater Sensor Networks (UWSNs) and Underwater Acoustic Networks (UANs), addressing critical challenges like energy constraints, security vulnerabilities, limited bandwidth, and interference. The proposed system integrates a Multi-Agent System (MAS), blockchain technology, and acoustic communication to enhance security, optimize energy usage, and improve data transmission efficiency. Key features include intelligent node mechanisms, proactive bandwidth and interference management, a multi-hop paradigm, distance-aware longevity strategies, and robust cryptographic protocols. The system is benchmarked against established routing protocols such as GCORP, PER, MARL-MC, and MLAR, demonstrating superior performance. The proposed cognitive intelligence (CI) protocol achieves energy consumption below 120 J per transmission, significantly lower than existing methods. It also achieves end-to-end latency under two seconds in multi-hop scenarios, outperforming alternatives like MARL-MC and GCORP. Additionally, the CI protocol exhibits a packet delivery ratio (PDR) exceeding 90% and an extended network lifetime surpassing 1850 s, making it a robust solution for resource-constrained underwater environments. This work not only addresses the unique demands of underwater networks but also contributes to the vision of self-sustainable and intelligent communication systems, aligning with the broader context of 6G paradigms through energy-efficient routing, cognitive intelligence, and secure, adaptive communication frameworks. The results underscore the effectiveness of the CI protocol in enhancing energy efficiency, reducing latency, and ensuring reliable long-term operation, thereby supporting critical applications like disaster management and environmental monitoring.

Objectives/Scope In light of the global transition towards the 1.5-degree pathway, the need for alternative power sources becomes increasingly crucial. However, the rapid growth in the number of wells has led to a substantial rise in power consumption. Currently, ESP systems account for a significant portion, approximately 19%, of the company's power consumption, with an upward trend. To address this challenge, this study aims to employ data analytics and Six Sigma tools to identify and implement methods for optimizing power consumption and enhancing energy efficiency within ESP systems. Methods, Procedures, Process The study commenced by gathering data for approximately 20 ESP parameters along with corresponding power consumption readings from a comprehensive sample of 250 wells. The data underwent meticulous verification and validation processes, ensuring technical and analytical accuracy. To gain insights into the relationship between power consumption and ESP parameters, graphical tools were employed for in-depth analysis. The analysis revealed two primary parameters that consistently contributed to higher power consumption in ESP systems when compared to their design specifications. The team then embarked on developing models aimed at concurrently reducing these identified parameters, namely voltage and tubing head pressure, without compromising production levels. The models demonstrated a promising reduction in power consumption, ranging from 8% to 24%, based on a sample of wells. Subsequently, the team implemented the identified optimizations on the selected wells, which resulted in tangible reductions in power consumption. Encouraged by these positive outcomes, the exercise was replicated across other wells within the field, yielding significant improvements in energy efficiency. Results, Observations, Conclusions The impact of the power consumption optimization measures implemented across the entire company's ESP systems, encompassing approximately 1,000 units equipped with variable speed drives, has yielded remarkable results. Calculations based on electricity tariffs indicate an annual cost savings of $3.0 million. This substantial reduction in operational expenses not only enhances the financial performance of the company but also reinforces its commitment to sustainability. In terms of environmental impact, the power reduction initiatives have led to a significant reduction of approximately 5,500 tons of CO2 emissions annually. The successful implementation of optimizations yielded significant financial savings and environmental benefits. This study establishes a foundation for future energy management strategies, emphasizing the potential for widespread adoption in the oil industry. Novel/Additive Information The study's noteworthy aspect is the successful integration of Six Sigma tools and data analytics, providing a structured framework for optimizing ESP systems. This approach enhanced clarity and direction, making the comprehensive analysis of parameters manageable. Critical factors influencing power consumption were identified, leading to accurate models and significant energy savings. The synergy between data analytics and Six Sigma showcases a novel and additive approach for optimizing energy management in ESP systems, with potential for broader application in complex systems across industries.

This review paper critically examines energy efficiency schemes in Non-Orthogonal Multiple Access (NOMA) systems, focusing on critical topics such as the integration of NOMA with cutting-edge technologies like Multi-Input Multi-Output (MIMO), beamforming, Mobile Edge Computing (MEC), and Reconfigurable Intelligent Surface (RIS), and discusses the challenges in radio resource allocation and environmental sustainability. The review aims to summarise the current state-of-the-art techniques, evaluate their performance, and identify future research directions to enhance energy efficiency in NOMA systems. This paper addresses several key findings: power allocation methods can enhance energy efficiency by up to 30% in specific scenarios; relay-assisted and hybrid NOMA schemes can boost data rates by 20–25%; and energy harvesting shows potential for improving energy efficiency, reducing power consumption by up to 15% in certain cases. These challenges include system complexity, which can increase the computational overhead by 40% when optimising resource allocation, and user fairness, where trade-offs in power allocation can reduce the data rate up to 10% for edge users in certain scenarios, ultimately affecting overall energy efficiency.

Intelligent technologies have advanced significantly over the past two decades and have integrated with cities to enhance citizen lives. Additionally, the amount of energy consumed varies depending on the weather, the number of occupants, and the type of building—commercial, residential, or administrative. In contrast, the citizen must make a trade-off between the building's environmental impact, comfort levels, and energy use. In this essay, we'll suggest a smart model that enables management, control, and regulation of energy usage in accordance with a set of standards. As a result, this approach enables real-time calculation, regulation, and optimization of energy usage as well as comfort for the occupants. As a result, the person can learn about their energy consumption without having to read electricity measurements or wait for a billing cycle. Additionally, this method enables energy resource conservation and increases system output even during periods of high demand.

This paper presents a detailed investigation into enhancing the energy efficiency of wastewater treatment plants (WWTPs) by integrating photovoltaic (PV) systems, emphasizing power flow analysis and experimental validation. Recognizing the substantial energy demands of aeration processes in WWTPs, this study proposes an innovative integration of PV panels with aeration tanks. This approach generates renewable energy and optimizes energy use through the thermal interaction between the PV panels and the aeration tanks. Key findings demonstrate a 15% overall increase in energy efficiency and a 5% improvement in PV efficiency due to aeration-induced cooling, along with a reduction in voltage fluctuations by up to 30% during high-demand periods. Additionally, the integration offsets approximately 20% of the WWTP's total energy consumption. The research is structured into two main components: a comprehensive power flow study using DIgSILENT PowerFactory and a laboratory experiment to validate the integration's effectiveness. The power flow analysis evaluates the electrical impact of PV integration on the WWTP's power grid, focusing on scenarios such as load fluctuations, grid disturbances, and the synchronization of PV generation with plant energy needs. The simulation results indicate that the integration significantly enhances the stability and efficiency of the plant's electrical system, reducing reliance on traditional energy sources. Concurrently, a laboratory experiment explored the practical effects of integrating PV systems with aeration tanks. The experiment demonstrated that the cooling effect provided by the aeration tanks leads to increased PV efficiency and notable energy savings. These experimental results align with the simulation findings, confirming the efficacy of this integrated approach. This study introduces a novel methodology for integrating renewable energy technologies into industrial processes, showcasing the potential for significant energy savings and improved operational efficiency in WWTPs. Future research will focus on scaling this integration strategy and assessing its long-term impacts on energy efficiency and wastewater treatment effectiveness.

The digital economy, emerging as a new form of economic system, holds a crucial position in enhancing energy efficiency. It does so by streamlining the distribution of resources with the help of data and augmenting the flow efficiency of production factors. This paper examines the precise role and mechanism of the digital economy in enhancing energy efficiency from both theoretical and empirical perspectives. This paper utilises panel data from China's provinces and cities over a fifteen-year period, ranging from 2006 to 2021. The fixed-effects model is employed to investigate the precise pathway in which the level of digital economy development influences energy efficiency. The study reveals that the digital economy has a notable impact on enhancing energy efficiency. The analytical mechanism indicates that the digital economy effectively improves energy efficiency by advancing regional marketisation, optimising the industrial structure, and fostering innovation and the adoption of new technologies. Owing to considerable disparity among diverse parts of China, the digital economy seems to have a positive impact on the eastern and central regions, while its influence remains restricted in the western regions. Meanwhile, the areas encompassed by the Yangtze River Economic Belt are the most viable for advancement in energy efficiency through the digital economy. The influence of the digital economy on energy efficiency is markedly inconsistent due to the considerable variation in resource allocation among various regions. The role of the digital economy in enhancing energy efficiency is particularly evident in regions with moderate resource endowment. Thus, this study contends that realisation of the digital economy's capacity requires the government to consider the regional development disparities and resource endowment circumstances. To achieve balanced regional growth and comprehensive progress in energy efficiency, the government must develop tailored policies that promote marketisation, optimise industrial structure, and incentivise technological advancement. Therefore, the implementation of these measures is crucial.

Phase Change Materials (PCMs) are a transformative technology revolutionizing construction, enhancing energy efficiency and thermal performance. PCMs, functioning as "thermal batteries" by absorbing and releasing thermal energy during phase transitions, store heat during warmth and release it as temperatures drop. This paper investigates PCM applications, selection criteria, integration methods, and classifications in construction. Integrated into materials like concrete and insulation, PCMs offer benefits such as enhanced energy efficiency, thermal comfort, passive cooling/heating, and reduced peak energy demand. Addressing construction's high energy consumption and environmental impact, PCM integration curtails energy use, fostering sustainability. Diverse integration approaches, including PCM-enhanced insulation and HVAC systems, cater to varied designs, climates, and energy requirements. PCM selection hinges on thermodynamics, chemical properties, accessibility, affordability, and kinetics. Categorized as organic, inorganic, and eutectic based on phase transitions, PCM selection is vital for optimal performance. Investigating the influence of Advanced Microencapsulated Phase Change Material (AMIC-PCM) on mortar, this study examines its effects on workability, density, and mechanical properties. AMIC-PCM's fine nature reduces workability, necessitating superplasticizers. It diminishes density and weakens compressive/flexural strength due to lower intrinsic strength. AMIC-PCM-incorporated mortar's thermal properties reveal intriguing trends in thermal conductivity and heat capacity. Small AMIC-PCM doses enhance properties through void filling, while larger doses lower thermal conductivity due to ingredient replacement. AMIC-PCM's thermal stability and characterization highlight its potential in construction materials. PCM integration in construction provides energy-efficient, sustainable solutions. PCM selection, integration, and material effects are vital for optimization. Study results underscore the significance of meticulous dosing and balance to achieve desired thermal attributes while preserving structural integrity.

Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT