Operation Optimization Research Articles

EIS Analysis and Optimization Using AI for Different Cathode Channel Cross-Sections for Open Cathode Fuel Cell Stack

Unlike convention polymer electrolyte fuel cell (PEMFC), open-cathode polymer electrolyte membrane fuel cells (OC-PEMFC) use oxidant supply fed directly from the atmosphere, owing to its simple balance of plant (BOP) and integration. Whereas concerns such as lower performance compared to PEMFC, needs to be studied carefully to understand mechanisms and parametric optimization for efficient operation of OC-PEMFC stack.The performance of OC-PEMFC is restricted due to proton conductivity, reaction mechanism, and mass transport limitation1. The effective flow-field (FF) designs can mitigate the detrimental performance degradation arising from dehydration in OC-PEMFC by effective cooling rate2. The simplified designs with optimum pressure drop enhances the performance with balanced effect of parameters such as gas distribution, water removal/retention, heat removal/accumulation.To enhance the performance of OC-PEMFC, we need to understand the physical and chemical mechanisms of the system. These mechanisms help to understand the losses involved caused due to low protonic conductivity, Oxygen reduction reaction, mass transfer, operating conditions, and nature/composition of materials3 etc. Among all electrochemical techniques, Electrochemical impedance spectroscopy (EIS) stands out as a powerful technique that provides a vast extent of information in a short period of time.The presented work investigates the performance of three distinct novel cathode channels with various cross-section designs (CSDs) such as square, boot, and triangular for four cells OC-PEMFC stack of active area 100 cm2. The analysis incorporates EIS examination for critical parameters, including airflow rate, current, temperature, H2 gas condition (humidified and dry). Furthermore, a parametric optimization technique is developed based on EIS and Artificial intelligence (AI) random forest regression (RFR) algorithm, the flow chart illustrating the RFR algorithm is presented in Fig. 1(a).The findings show that, for square design the minimum low frequency resistance 7 Ω cm2 is achieved at airflow rate 3 m3/sec, current 10-15 A, and stack temperature ~ 39 ̊C, as shown in Fig 1(b). Moreover, the AI algorithm successfully predicted the suitable operating condition with root mean square error value of 98%, as illustrated in Fig. 1(c). References J. C. Kurnia, B. A. Chaedir, A. P. Sasmito, and T. Shamim, Appl Energy, 283, 116359 (2021).S. Thapa, V. Ganesh, H. Agarwal, and A. K. Sahu, J Power Sources, 603, 234398 (2024).Z. Tang, Q. Huang, Y. Wang, F. Zhang, W. Li, A. Li, L. Zhang, and J. Zhang, J Power Sources, 468, 228361 (2020). Figure 1

Theoretical Model and Control-Parameter Classification for Solid Oxide Fuel Cell System with Cooling Air Bypass into Burner

When using solid oxide fuel cells (SOFCs) as the auxiliary power unit (APU) of automobiles, a stringent thermal control to limit the temperature and thermal stress of all components of the SOFC system is required. Various thermal management methods have been proposed, mostly involving the use of cooling air (CA). To avoid the thermal shock associated with directly adding CA into the SOFC stack air inlet stream, directing CA into the burner is a desirable alternative. Using CA bypass into the burner is a simple and effective way to control the excessive burner temperature, which in turn limits the temperatures of other system components.In this study, a 5 kW SOFC system fueled by diesel and with CA into the burner is examined. The system consists of an SOFC stack and a balance of plant (BOP) that includes a steam generator, an air heat exchanger, a fuel heat exchanger, a pre-reformer and a burner, a water pump and an air blower. The burner burns the unused fuel and provides heat to the pre-reformer and heat exchangers. A thermocouple is installed near the outlet of the burner, which is connected to a CA pipe. When the burner temperature exceeds its set maximum temperature, TBM, the CA valve opens to allow fresh air to enter directly to the burner through the bypass pipe to maintain the burner temperature at the set value. A comprehensive theoretical model, including detailed sub-models for all system components, is developed and verified with multiple experimental data.The theoretical model is used in a comprehensive control-parameter classification analysis, revealing the impacts of the fuel utilization, Uf, air flow rate, Vair, and TBM = (900 ℃, 1000 ℃) on the system performance. In particular, the effects of (Uf, Vair, TBM) on the system electrical efficiency, Esys, and the characteristic temperatures of the stack and BOP components are determined quantitatively. The main findings are: 1) It is confirmed that the CA bypass into the burner can limit the operating temperatures of all system components. 2) Changing TBM from 900℃ to 1000℃ can notably increase Esys only for low Uf, but may increase the maximum stack temperature, TM. It appears that TBM = 900℃ is more favorable than TBM = 1000℃. 3) Increasing Uf increases Esys, but may also increase TM and the maximum stack temperature difference, Tdiff. 4) TM and Tdiff as well as Esys decrease with the increase of Vair. Overall, the constrains of (TM ≤ 900℃, Tdiff ≤ 200℃) can be satisfied together with Esys > 55%, if Vair is chosen properly.It is concluded that the examined system design is suitable as an APU. Moreover, the system model presented here can be used for the design and operation optimization of similar systems, taking factors such as the stack size and performance, the thermal constrains the stack can withstand into considerations.

Intelligent control system and operational performance optimization of a municipal solid waste incineration power plant

This study proposed an integrated intelligent control system in one municipal solid waste incineration power plant to improve the system's control accuracy, economic performance and environmental impact, including the intelligent combustion, intelligent denitrification, intelligent desulfurization and intelligent soot-blowing control subsystems. The precise detection and adjustment of the key operational parameters was achieved by integrating these modules into the existing distributed control system. A comparative analysis of the operation data between pre-optimization and post-optimization states was conducted to assess the changes in the key operational and economic parameters. The results indicate that the introduction of the intelligent control module can significantly improve the system stability and parameter control accuracy, thereby enhancing the economic efficiency of the waste incineration power plant and reducing the operation workload and the pollutants emissions. Specifically, the standard deviations of main steam flowrate and pressure decreased by 45.1 % and 60.7 %, respectively. Furthermore, the consumptions of the ammonia water and lime slurry were reduced by 38.2 % and 23.2 %, respectively, while the auxiliary power consumption rate declined by two percentage points, and the power generation per ton of waste increased by 4.2 %. These improvements not only strengthen the economic benefits but also effectively reduce the pollutants emissions.

AI in Manufacturing: Driving Operational Excellence

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, focusing on key operational areas, including automated operations management, predictive maintenance, advanced production planning, and quality control enhancement. The article examines how AI technologies revolutionize traditional manufacturing processes through intelligent automation, data-driven maintenance strategies, sophisticated production planning, and enhanced quality control systems. The article demonstrates significant improvements in operational efficiency, maintenance optimization, production accuracy, and product quality across diverse manufacturing environments. Through detailed analysis of implementation cases and industry studies, this paper illustrates how AI integration drives operational excellence while creating new paradigms in intelligent manufacturing systems.

Principles of operational optimization of CSP plants based on carbon dioxide mixtures

This research, developed in the framework of the SCARABEUS project, studies the off-design performance of transcritical power cycles running on C2-S2 mixtures in Concentrated Solar Power applications. The objective of this work is to identify optimum operational strategies that maximize net energy production when exposed to variable ambient temperature, with special focus on the operation of the Heat Rejection Unit (Air-Cooled Condenser).Four different strategies are identified, depending on ambient temperature: variable or constant condensation pressure for ambient temperatures lower than the design value, and constant turbine inlet temperature or constant return temperature of the heat transfer fluid for ambient temperature higher than design value. The results show that a combination of variable and constant minimum cycle pressure stands as the most promising alternative for low ambient temperatures, enabling net system efficiencies higher than 41%. As a drawback, this strategy poses potential issues on the control of the compressor inlet, which must be further investigated in future works. On the other hand, constant turbine inlet temperature enables higher net performance than constant return temperature of the heat transfer fluid, even if at the expense of a reduction in energy storage capacity for the same inventory of molten salts and the creation of detrimental thermal transients. Finally, it is found that the Air-cooled condenser configuration with all fans equipped with Variable Frequency Driver stands as the best alternative to maximize the techno-economic performance of the system.

Analysis and Optimization of Cryogenic Distillation Systems: For Reducing Distillation Energy Consumption

AbstractCarbon capture utilization and storage‐enhanced oil recovery (EOR) is often considered the most promising technology for utilizing CO2. Cryogenic distillation is also viewed as the most reasonable separation option for handling CO2‐EOR gases, despite being an energy‐intensive process. The main challenge for this technology is energy loss. To overcome this challenge, one potential alternative is to optimize the system processes and parameters. This study proposes a new process to reduce distillation energy consumption by refluxing the distillate back to the distillation column. Operational parameter optimization was performed using the commercial simulator Aspen HYSYS for modeling and sensitivity analysis of process parameters using orthogonal experimental methods. The simulation results indicate that after optimization, the energy consumption in the distillation process decreased from 0.207 to 0.196 MJ kg−1, whereas the purity decreased slightly from 94.63 % to 94.52 %. However, the recovery increased from 97.8 % to 97.88 %, and the total energy consumption decreased from 0.772 to 0.761 MJ kg−1.

Harnessing AI & SaaS-Based Enterprise App Development for Business Growth

Purpose: This paper aims to explore the transformative impact of artificial intelligence (AI) on enterprise models within the context of Software as a Service (SaaS), highlighting the necessity for organisations to adopt AI-driven strategies to sustain their competitive edge. Methodology: The study utilises a comprehensive framework that outlines five critical steps for implementing AI in business settings, including executing pilot projects, assembling in-house AI teams, providing extensive training, developing holistic AI strategies, and ensuring effective communication. Findings: The integration of AI within SaaS not only enhances operational efficiency and process optimisation but also allows for real-time customisation and improved user engagement, thus fostering customer loyalty and driving sustainable growth. Unique contribution to theory, practice and policy: The paper contributes to the understanding of AI and SaaS as essential components in the evolution of enterprise applications, proposing that organisations adopt a collaborative approach to harness their combined potential, ultimately informing policymakers about the strategic importance of AI in shaping future business landscapes.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Uncertainty in underground mining operations: a bibliometric and systematic literature review analysis

Purpose This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024. Design/methodology/approach To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations? Findings As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines. Originality/value This research is 100% original because there is no review research on the uncertainty present in underground mining operations.

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Low-carbon optimization of multi-microgrid system operation considering supply–demand bidirectionality based on Nash bargaining

Optimization of batch cooling crystallization systems considering crystal growth, nucleation and dissolution. Part I: Simulation

Optimal control of batch crystallization systems is still a focus and hot topic in the field of industrial crystallization, which seriously affects the consistency of batch product quality. In this paper, a new method with a new objective function and improved optimization algorithm was proposed for optimization of crystal size distribution (CSD) in case of fine crystals occurrence. The new objective function was developed with better margin metric and weighting technique to minimize fine crystal mass, meanwhile, a newly constructed sinusoidal weight function was introduced to improve the particle swarm optimization (PSO) algorithm. A precise control of CSD with suppressed numerical discrepancy caused by fine crystals removal was developed by combining seed recipe and temperature-swing. In addition, the effects of temperature curve segments on CSD during process optimization were systematically investigated to achieve optimal results. Two typical batch cooling crystallization systems were used to verify the effectiveness of the proposed method in controlling product CSD while minimizing fine crystal mass. Results demonstrated that the desired product CSD can be achieved with minor errors while the fine crystals could be shrunk to be negligible, i.e., the fine crystal mass and number can be reduced by over 90%. This work has an important guiding significance for the removal of fine crystals in industrial crystallization processes, especially when only operational optimization rather than equipment updating is considered.

Drop breakup can occur inside the gap of a high-pressure homogenizer – New evidence from experimental breakup visualizations

High-pressure homogenizers are used extensively in food and beverage processing. For design and operation optimization, it is vital to understand where breakup takes place. Using an experimental high-speed camera setup on a scaled model, this contribution shows that breakup events can occur inside of the gap of a homogenizer valve. This contrasts with many previous studies which only observe breakup downstream of the gap. Results show that gap breakup occurs at a substantially lower rate than outlet chamber breakup. Comparisons with computational fluid dynamics suggest that a necessary (but not sufficient) condition for gap breakup, is that the drop passes into the high-shear region close to the separation zone at the seat wall. Implications for food emulsification are discussed.

Analysis and optimization of the efficient press shop operation

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.

Multi-Cloud Automation : A Strategic Approach to Cloud Infrastructure Management

This article examines the evolution and impact of multi-cloud automation strategies in modern enterprise environments, supported by comprehensive industry research and case studies. Drawing from multiple industry reports, including Flexera's 2024 State of the Cloud Report and PwC's Cloud and AI Business Survey, the research reveals that 89% of enterprises now employ multi-cloud strategies, with the global multi-cloud management market projected to grow at a CAGR of 27.3% through 2030. The article analyzes key components of successful multi-cloud automation, including deployment automation, operational excellence through monitoring, and incident management, while providing detailed metrics on their effectiveness. Through examination of real-world implementations across retail, financial services, and SaaS sectors, the research demonstrates how organizations achieve significant improvements in operational efficiency, cost optimization, and business agility through comprehensive automation strategies.

Integrated Hybrid Modelling and Surrogate Model-Based Operation Optimization of Fluid Catalytic Cracking Process

Fluid Catalytic Cracking (FCC) is one of the most important conversion processes in oil refineries, widely used to convert high-boiling, high-molecular-weight hydrocarbon components from crude oil into more valuable products like gasoline and diesel. Advanced simulation and optimization technologies are critical for improving the operational efficiency and economic performance of the FCC process. First-principles-based simulators rely on parameter estimation and are computationally intensive, making them unsuitable for online optimization. In recent years, with the development of deep learning, data-driven models have made significant progress in FCC modeling. However, due to their black-box nature and difficulty with extrapolation, they are rarely used for optimization. To bridge this gap, we propose an integrated framework that combines hybrid modeling and surrogate model-based optimization. This approach combines plant and simulation data to train a multi-task learning prediction model, which then serves as a surrogate for operational optimization. Validated on a large-scale FCC unit in southern China, the model predicts product yields with an error margin of under 4.84% for all products. Following optimization, yields of LNG, gasoline, and diesel rose by an average of 0.10 wt%, 1.58 wt%, and 1.05 wt%, respectively, resulting in a 3.67% increase in product revenues. This highlights the substantial potential of this framework for industrial applications.

Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

Multi-objective optimisation of ORC–LNG systems using the novel One-shot Optimisation method

Optimisation is drawing more and more attention in the organic Rankine cycle (ORC) research field. However, as the complexity of the ORC scenarios increases, it poses challenges on operational parameter, working fluid, and configuration optimisation levels. This work first proposes an improved optimisation method, termed the One-shot Optimisation (OSO) method, which can simultaneously optimise the working fluid and configuration. Then, a two-objective optimisation is performed using the OSO method in combined ORC–LNG systems, considering up to eight operational parameters, 11 working fluids, and 16 system configurations to maximise energy efficiency and minimise the electricity production cost (EPC). Finally, the result of the optimisation is divided according to the thermodynamic weight (W1), and two typical conditions are analysed in detail: the maximum case (W1 = 1) and the balanced case (W1 = 0.5). The results show that the OSO method is capable of identifying the optimal working fluid and optimal configuration within a single optimisation process. The basic ORC configuration is preferred when W1 is lower while the recuperative ORC is preferred when W1 is higher. The balanced case can achieve an energy efficiency comparable to that of the maximum case but with a significantly lower EPC. The balanced case can achieve as much as 87. 48% of energy efficiency, requiring only 19.77% of the EPC compared to those of the maximum case.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approach. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Case Study on The Operational Optimization of The Financial Shared Services Center at Huahe Real Estate

During the period of reform and opening-up in China, the real estate industry has experienced rapid development. Since the housing system reform began in 1998, an increasing number of enterprises have entered the real estate sector. Most well-known large real estate companies emerged during this time. Until 2007, the real estate market was in its "golden era," where companies could enhance their profits solely through land appreciation. However, as more companies entered the market, available land for construction became scarcer, competition intensified, and the government's regulatory policies on the real estate industry increased, making it more challenging for real estate companies to earn profits as easily as before. In recent years, the government has introduced the concept of "housing is for living, not for speculation," strengthening its control over the real estate sector. Consequently, real estate companies have begun to transform their profit models in an effort to continue generating profits.This article analyzes the operations of the financial shared services center at Huahe Real Estate and identifies issues such as a high core business order cancellation rate, low internal operational efficiency, a high employee turnover rate at the shared center, and problems related to information technology. The third section analyzes the causes of these issues from both internal and external perspectives. Internally, the factors include strategic structure, business processes, personnel management, and information systems. Externally, the primary causes are related to national policies and technological changes. The fourth section proposes corresponding policy recommendations based on the identified issues and their causes. It is hoped that this analysis will provide insights for the ongoing operations of the financial shared services center at Huahe Real Estate, as well as offer case support for other real estate companies looking to establish financial shared services centers.

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.