Abdulrahman M. Abomazid, Nader A. El-Taweel, H. Farag
The production of renewable hydrogen using water electrolysis has emerged with the increasing penetration of renewable energy sources. The energy management system (EMS) plays a key role in the production of renewable hydrogen by controlling electrolyzer’s operating point to achieve operational and economical benefits. In this regard, this article introduces the optimal scheduling for an EMS model for a hydrogen production system integrated with a photovoltaic (PV) system and a battery energy storage system (BESS) to satisfy electricity and hydrogen demands of an industrial hydrogen facility. The proposed EMS model aims to minimize the cost of hydrogen (CoH) production by minimizing the system net costs of industrial hydrogen facility while maintaining a reliable system operation. Furthermore, the proposed EMS model enables the application of seasonal hydrogen storage by incorporating the Z-score statistical measure of historical electricity prices, which follows seasonal electricity price trends. This allows the storage of hydrogen during periods of relatively low electricity prices. To demonstrate the validity of this model, it is tested for both intraseasonal and seasonal storage. Four case studies are used to prove the techno-economic benefits of the proposed EMS model. Furthermore, the impact of the electrolyzer’s capacity factor, the size of the hydrogen storage, and the PV share is investigated in terms of their techno-economic benefits to the system.
Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations.
Haoran Qiu, Subho Sankar Banerjee, Saurabh Jha
et al.
Modern user-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is still challenging in production because contention for shared resources can cause latency spikes that violate the service-level objectives (SLOs) of user requests. This paper presents FIRM, an intelligent fine-grained resource management framework for predictable sharing of resources across microservices to drive up overall utilization. FIRM leverages online telemetry data and machine-learning methods to adaptively (a) detect/localize microservices that cause SLO violations, (b) identify low-level resources in contention, and (c) take actions to mitigate SLO violations via dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up to 16x while reducing the overall requested CPU limit by up to 62%. Moreover, FIRM improves performance predictability by reducing tail latencies by up to 11x.
The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.
Abstract Mining is very vital to the production of goods, services and infrastructure; it advances the quality of lives in the society. However, the possible hazard of waste and radioactivity generated by mining, dumping and tailing, has called on the society to find ways of seeking remedy that will adequately treat mining waste from mine dump, tailing and abandoned mine. Mine waste reuse and recycling in mining industries could offer cost-effective benefits through offsetting raw material requirements and decreasing the volumes of waste to be managed. This review discussed mine dump pollution monitoring and mine dump management strategies for some selected countries. Impact and mechanism of mine damage to the environment was discussed together with the remediation principles. It further examines the mining Act and regulations of the same selected countries. Emphasised was placed on the enforcement of environmental laws, regulations, and standards. Practical ways in which country’s state authority and civil society can keep a close watch and enhance the enforcement of laws and regulations were highlighted. The prediction for the control of mineral exploration and environmental assessment was also discussed for executing a specific control to take preventive measures. Management techniques used in combating the impact of mine dump, stockpiles and tailing on the environment were discussed. In addition, radioactivity in mine and its monitoring and control was discussed.
B. Bharathiraja, T. Sudharsana, J. Jayamuthunagai
et al.
In the prevailing scenario, the aberrant use of conventional fuels and the impact of greenhouse gases on the environment have leveraged the research efforts into renewable energy production from organic resources and waste. The global energy demand is high and most of the energy is produced from fossil resources. Recent studies refer the anaerobic digestion (AD) as alternative and efficient technology which combines biofuel production and sustainable waste management. There are different technological trends in biogas industry in order to enhance the production and the quality of biogas. Nevertheless, the success of AD for further investments will rise from the low cost of feedstocks availability and the wide variety of usable forms of biogas (heating, electricity and fuel). Biogas, a combination of two-thirds of methane (CH4) and the rest is mostly carbon dioxide (CO2) with traces of hydrogen sulfide. The spent slurry from the produced biogas can be enriched to be utilized as manure for agricultural crop, promoting sustainable biomass production in the world. Biogas can be utilized to produce centralized or distributed power supply in rural and urban areas and are considered to be cost beneficial. The aim of this review paper is to analyze various feedstocks, which are widely used all over the world. The working operations of anaerobic digestion process, current trends along with its merits and demerits are also discussed in order to draw more research and development towards producing a sustainable environment.
In the post-pandemic era, multi-sourcing is rapidly becoming the preferred approach for companies to drive optimized cost, quality and turnaround times. However, multi-sourcing systems present a myriad of challenges in designing an effective planning model. In this article, we propose a robust capacity planning model with multiple supply sources, and demonstrate how the capacity plan can be efficiently solved via a parsimonious mean–variance approach. We apply the max-min criterion to design a distributionally robust multi-source capacity plan. We use a new approach to deriving a set of optimality conditions on the robust capacity plan. These conditions enable us to derive the worst distribution, the optimal robust capacity vector, and the worst-case expected profit in closed form. The new approach bypasses numerous tedious intermediate procedures in the traditional distributionally robust optimization literature and allows us to derive the optimal solution based on exogenous cost parameters. This closed-form solution appears to be hitherto unknown and has important ramifications for the multi-sourcing capacity planning problem. Our findings reveal that, despite the complexity of multi-sourcing, the worst-case demand scenario can be reduced to just n + 1 distinct outcomes, structured by two key sequences derived from supplier characteristics. The optimal capacity plan has a clear structure–allocations align with the midpoints between these demand scenarios. Surprisingly, we also find that most of the robustness benefits of a full supplier portfolio can be achieved by engaging just the two best-matched sources. This provides a practical and cost-effective road map for robust capacity planning in multi-sourcing environments.
Prior to the Internet, many industries catering to mass audiences, particularly entertainment industries, tended to have very skewed sales distributions with most sales volume accruing to a small share of products (titles) generally known by terms such as “blockbusters.” The long tail hypothesis claims that increased Internet usage will weaken the dominance of blockbusters in these industries, so that the large number of obscure products, in aggregate, will become an important, perhaps primary, portion of the market. Such a change would have significant implications for firms’ operations. We test this hypothesis for the book industry with two analyses. First, using unusually rich data on book sales, we examine the temporal share of sales taken by niche titles as Internet retailing grew from obscurity to ubiquity. Changes in the distribution of sales by title popularity are weakly consistent with the direction of the hypothesis but not with the magnitude. Second, we exploit the 2011 bankruptcy of the Borders bookstore chain, and the resulting sudden shutdown of all its physical stores as a natural experiment. This analysis also finds a shift in sales away from the most successful titles. But the major beneficiaries are not the most obscure titles but instead titles that would regularly be found in most bookstores. Both analyses support a version of the long tail where the share of obscure titles changes from one very small value to a somewhat greater but still very small value. These results are inconsistent with proposed radical changes in management practices to accommodate a presumed large increase in product variety.
After a long period of little change, glaucoma surgery has experienced a dramatic rise in the number of possible procedures in the last two decades. Glaucoma filtering surgeries with mitomycin C and glaucoma drainage devices remain the standard of surgical care. Other newer surgeries, some of which are minimally or microinvasive glaucoma surgeries, target existing trabecular outflow, enhance suprachoroidal outflow, create subconjunctival blebs, or reduce aqueous production. Some require the implantation of a device such as the iStent, Hydrus, Ex‐PRESS, XEN and PRESERFLO, whilst others do not—Trabectome, Kahook dual blade, Ab interno canaloplasty, gonioscopy‐assisted transluminal trabeculotomy, OMNI and excimer laser trabeculotomy. Others are a less destructive variation of an established procedure, such as micropulse transscleral cyclophotocoagulation, endoscopic cyclophotocoagulation and ultrasound cycloplasty. Cataract surgery alone can be a significant glaucoma operation. These older and newer glaucoma surgeries, their mechanism of action, efficacy and complications are the subject of this review.
Meryem Meliani, A. E. Barkany, I. E. Abbassi
et al.
Integration of distributed generations that fluctuate widely (such as Photovoltaic panels, Wind power, Electric Vehicles and Energy Storage Systems), poses a chance to the stability of power technology and distribution structures. However, the primary reason is that the electricity ratio between supply and demand may not be balanced. An extra or scarcity inside the production or intake of electricity can disrupt the system and cause critical problems which include a drop/rise in voltage and, under difficult conditions, power outages. The use of Energy Management Systems can effectively increase the balance between supply and demand and decrease peak load throughout unplanned durations. The energy management system is capable of not only sharing or exchanging energy between the different energy resources available, but also of economically supplying loads in a reliable, safe and effective manner under all conditions necessary for the operation of the power grid. This work outlines the structure, goals, benefits and defies of the energy control system via an in-intensity analysis of the distinctive stakeholders and participants engaged on this system. A detailed essential analysis of the functioning of distinct programs which includes Demand Response, Demand Management and Energy Quality Management implemented inside the electricity management gadget is presented in this review. It also summarizes quantifications of the various strategies of uncertainty. It includes as well a comparative and an important assessment of the primary optimization techniques which are used to obtain the extraordinary goals of energy management structures while at the same time meeting a wide range of requirements.
Stocks of some food products, such as whiskey, cheese, or port wine, ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Inventory management decisions include purchasing volumes of agricultural produce and production volumes for age-differentiated products. Because products can be blended from stocks of different ages, issuance decisions offer operational flexibility. However, whereas some industries (port wine, sherry) only request that the product labels refer to the average age of issued stocks, others (whiskey, rum) have stricter blending regulations, requiring that the product labels represent the minimum age of all components. Further, producers must deal with multiple uncertainties. Purchase prices of agricultural commodities depend on volatile climate-dependent harvest seasons, stocks decay during maturation, and sales market conditions fluctuate. We solve this inventory management problem using a deep reinforcement learning algorithm with three key innovations: (i) A novel actor pipeline that decomposes the action space and flexibly partitions decision dimensions between a neural network and a lookahead optimization model, (ii) an algorithm explicitly maximizing average rewards, and (iii) reward-handling techniques that exploit structural problem insights. Our approach yields near-optimal policies that consistently outperform benchmark heuristics. Beyond the algorithmic contributions, our results offer new managerial insights into the value of blending under uncertainty. Minimum-age blending substantially enhances the profits of firms as compared to no blending because companies can adjust their purchasing policy in response to price fluctuations. The more flexible average-age regime further improves profits by 8.7 % on average, suggesting that whiskey and rum regulators may wish to reconsider their strict blending rules. We mine black-box policies from deep reinforcement learning using supervised machine learning and Shapley values to analyze near-optimal decision drivers. Exploiting the value of blending requires producers to install sufficient processing capacity, especially when dealing with large variations in harvest seasons. Additionally, blending entails increased planning complexity because the inventory management decisions are driven by a large number of factors.
This paper studies a simultaneous-search problem in which a player observes the outcomes sequentially, and must pay reservation fees to maintain eligibility for recalling the earlier offers. We use postgraduate program applications to illustrate the key ingredients of this family of problems. We develop a parsimonious model with two categories of schools: Reach schools, which the player feels very happy upon joining, but the chance of getting into one is low; and safety schools, which are a safer choice but not as exciting. The player first decides on the application portfolio, and then the outcomes from the schools applied to arrive randomly over time. We start with the extreme case wherein the safety schools always admit the player, and show that it suffices to focus on the last safety school. This allows us to conveniently represent the player’s value function by a product form of the probability of entering the last safety period and the expected payoff from then on. We show that the player’s payoff after applications is increasing and discrete concave in both the numbers of reach and safety schools, and the optimal number of reach schools increases in the reservation fee. The proof technique utilizes stochastic coupling, stochastic dominance, and directional monotone comparative statics arguments. We also develop a recursive dynamic programing algorithm when admissions to safety schools are no longer guaranteed. We demonstrate instances in which the player applies to more safety schools when either the reservation fee gets higher or the admission probability drops lower, and articulate how these arise from the portfolio optimization consideration.
Ljubomir Pupovac, Vivek Astvansh, François Carrillat
et al.
Following a manufacturer's large product recall, its supplier's shareholders may perceive uncertain future demand for the supplier's products and react punitively, causing a drop in the supplier's stock return—that is, a contagion (or negative spillover). Moreover, shareholders’ information asymmetry may cause them to “screen” the supplier's information cues to determine the supplier's extent of demand uncertainty. The ideal screen is the supplier's proportion of sales revenue from the recalling manufacturer. However, not all suppliers disclose this information. Therefore, we propose that shareholders use a two-stage screening. The first screen is whether the supplier demonstrates transparency by voluntarily disclosing information about its customer portfolio. The second screen—available only to the subset of suppliers that disclose customer information—is the supplier's sales revenue from the recalling manufacturer. We used a sample of 896 U.S. public manufacturer–supplier dyads impacted by 27 large manufacturer recalls. An event study followed by cross-sectional regressions provides evidence of contagion. In addition, it reveals that the supplier's voluntary disclosure of customer information mitigates contagion, whereas revenue dependence aggravates it. Contextual (i.e., recall) variables also impact contagion. Our research study contributes to the supply-chain contagion literature, screening theory, and customer information disclosure literature. The findings inform supplier firm managers that their prior customer-related disclosures and the contextual variables can moderate contagion.
An integrated approach is sorely needed to treat biogas emanating from anaerobic digesters (AD) which is cost-effective, in terms of upgrade/purification to ~95–98% methane needed for pipeline injection. This is a very pressing environmental and waste-management problem. At present, biogas water-/solvent-washing operations require significant capital investment, with high operational and maintenance costs. In the present study, we deployed a facile and efficient novel nanobubble-formation approach using applied electric fields to boost biogas-enrichment operations: we achieve substantial methane enrichment via selective CO<sub>2</sub> and H<sub>2</sub>S take-up in water in the form of nanobubbles. This enables an integrated waste-processing vision using cutting-edge engineering-science advances, and making anaerobic digestion a circular-economic and practical reality, that can be deployed at scale—initially developing at the small scale—and points the way for low-energy CO<sub>2</sub> capture in the form of nanobubbles by dint of the electric-field approach. In addition, we carried out nanobubble generation using various gases for water treatment for both up- and down-stream sludge-containing (waste)water, achieving meaningful operational successes in AD operations and organic-fertiliser production, respectively.
Gordan Mimić, Amit Kumar Mishra, Miljana Marković
et al.
Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017–2020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical–horizontal (VH) and vertical–vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an <i>R</i><sup>2</sup> score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.
This paper examines the transformative role of the Digital Twin-Computer Vision combination (DT-CV combo) in industrial operations, focusing on its applications, challenges, and future directions. It aims to synthesize the existing literature and explore the practical use cases in operations management (OM). A comprehensive systematic literature review is conducted using PRISMA guidelines to analyze the DT-CV combo across the classification of industrial OM. However, given the breadth and importance of manufacturing and the OM field, the study excludes the literature on the DT-CV combo applied to other domains such as healthcare, smart buildings and cities, and transportation. We found that the DT-CV combo in OM is a relatively young but growing field of research. To date, only 29 articles have examined DT-CV combo solutions from various OM perspectives. Case studies are rare, with most studies relying on experimentation and laboratory testing to investigate DT-CV applications in the OM context. According to the cases and methods reviewed in the literature, the DT-CV combo has applications in different OM areas such as design, prototyping, simulation, real-time production monitoring, defect detection, process optimization, hazard detection and mitigation, safety training, emergency response simulation, optimal resource allocation, condition monitoring, inventory management, and scheduling maintenance. We also identified several benefits of DT-CV combo solutions in OM, including reducing human error, ensuring compliance with quality standards, lowering maintenance costs, mitigating production downtime, eliminating operational bottlenecks, and decreasing workplace accidents, while simultaneously improving the effectiveness of training. In this paper, we classify current applications of the DT-CV combo in OM, highlight gaps in the existing literature, and propose research questions to guide future studies in this domain. By considering the rapid phase of AI technology development and combining it with the current state of the art applications of the DT-CV combo in OM, we suggest novel concepts and future directions. The digital twin-vision language model (DT-VLM) combo as a future direction, emphasizing its potential to bridge physical–digital interfaces in industrial workflows, is one of the future development directions.
This paper proposes a 2D axis-symmetric numerical model designed in COMSOL Multiphysics to estimate power demand along the secondary steelmaking process. The analyzed process has been divided into cycles and phases included ladle preheating, waiting, filling, heating, continuous casting, and cleaning. Each cycle is modeled through 17 sequential time-dependent numerical simulations, and has been validated by comparing calculated temperatures with those measured in a steel plant. The study observes that the volumetric power demand decreases progressively over the cycles, stabilizing at 1.95 MW/m3 after six cycles. Notably, the first cycle is by far the most power-consuming, accounting for nearly 20% of the total. Additionally, the ladle’s inner temperature drops by 300 °C within 30 min during the waiting phase between preheating and tapping, and by 400 °C within 55 min between two production cycles. The model is designed to qualitatively evaluate various factors associated with the secondary steel-making process, and it could be used within the framework of a DOE analysis for examining the impact of different variables to optimize energy management and other operations involving ladles.
Background: It is widely accepted that the digital transformation of business is increasingly attracting the attention of researchers from the academic circles as well as professionals from the business community. The main consequence of this lies in the daily development of new and improvement of existing digital technologies. The outcomes of such events on the market are reflected in all aspects of companies' operations. For this reason, they are constantly looking for various improvements to their business, which most often include the implementation of new technology. Mere implementation of a new technology without any other changes very often leads to failure. The core of this failure can be found and attributed to inadequately identified, analysed, documented and established business processes. Business process management (BPM) and redesign as its integral part are actually an indispensable segment of a successful process of digital business transformation. Therefore, the digital transformation of business should not be viewed exclusively from a technological perspective, but also from a process viewpoint. Purpose: With the aim of shedding additional light on the connection between business process management and digital business transformation, the paper aims to identify and explain the importance of business process redesign. Study design/methodology/approach: For the purposes of this paper, a systematic literature review was conducted. Findings/conclusions: The result of the conducted research indicates that a process approach to the digital transformation of business can contribute to significantly different, more successful results. Limitations/future research: Limitations refer to the number of databases searched during this systematic literature review. Subsequent research could include additional sources that would include additional works that can contribute to a better research result.
Production management. Operations management, Personnel management. Employment management
Abstract Agriculture significantly contributes to greenhouse gas emissions, necessitating swift policy action to mitigate its environmental impact, aligning with UN sustainable development Goals (SDGs). Assessing energy usage and targeting energy-intensive operations are key to effective energy management that enables farmers to implement strategies, reducing costs and carbon footprint. Implementing energy audits in agriculture requires a proper sampling methodology to identify energy-intensive operations and explore renewable energy sources. Therefore, the study presents a detailed sampling design and methodology for estimating energy usage in agricultural crops. It outlines sample size determination, allocation across strata, selection and parameter estimation procedures. Non-parametric data envelopment analysis (DEA) identified 7% of farm households as efficiently using energy, with an average technical efficiency of 0.77 for inefficient ones, suggesting a potential 23% resource saving without yield reduction. Additionally, CO2 emissions per crop production process highlight the urgency of policy formulation to promote renewable energy sources.