Production Unit Research Articles

Design, multi-aspect investigation and economic advantages of an innovative CCHP system using geothermal energy, CO2 recovery using a cryogenic process, and methanation process with zero CO2 footprint

The significance of devoting attention to environmental pollution control strategies is widely recognized as an effective means of mitigating the harmful environmental effects caused by fossil fuels in industrial and power plant sectors. Carbon dioxide (CO2) capture and recovery technologies have opened up the possibility of utilizing this pollutant gas. Hence, the current study suggests a methodology for the CO2 hydrogenation of the flue gas leaving a power plant. This approach facilitates methane generation via a methanation reactor, subsequently is utilized as fuel for a power plant. For this purpose, a cryogenic method using liquefied natural gas cold energy facilitates CO2 recovery. Moreover, the whole system utilizes geothermal energy to launch a power plant for power generation and supply the power demands of a hydrogen production unit, relying on a water electrolysis process. The hydrogen generated is employed for CO2 hydrogenation, while the oxygen produced is utilized for the combustion reaction. Heating provider units and an absorption chiller are also included in the design. The system is modeled utilizing Aspen HYSYS software. This study incorporates a sensitivity analysis alongside energy, exergy, environmental, and economic assessments. The energy and exergy efficiencies, as determined by the thermodynamic analysis, are 30.87% and 48.61%, respectively. Additionally, according to the economic study, the levelized energy cost amounts to 16.65 $/MWh, demonstrating a substantial reduction of 87.6% compared to the power generation mode. One notable merit of the suggested system lies in its zero CO2 footprint framework, showing a noteworthy supremacy when compared with previous studies.

Research on improving the automatic stability of trimming unit

Abstract Due to the influence of temperature and other factors in the forming of forgings, the shape of the forging fringe is often challenging to control and predict, which restricts the operation of forging automation. In this paper, the forging position is detected by the vision means, which is conductive to identify whether the production process is normal or not to avoid the loss caused by the interruption of the production unit. Meanwhile, for the forging temperature changes on visual recognition, the relationship curve between forging temperature and exposure time is determined by statistical method, so that the temperature of the forging regulate the visual camera exposure time to improve the accuracy of visual recognition.

Development of a Novel Process Towards an l-Malate Biorefinery Using Methanol as Feedstock

Malate is primarily obtained from fossil resources. However, growing environmental concerns are rerouting chemical manufacturing to biomanufacturing. Despite significant ‘discovery research’ efforts for sustainable malate production, a successful industrial implementation remains elusive. A novel methanol-based l-malate biomanufacturing process using the methylotrophic bacterium Bacillus methanolicus is designed and the relationship between R&D and techno-economic feasibility is assessed. Two scenarios are modeled and compared using SuperPro Designer®. First, with limited R&D success a 65g/L titer is achievable. Then with further strain improvement and process development titer is increased to 100g/L. Four independent design parameters; biomass formation, CO2 evolution, methanol loss and selling price are selected to analyze techno-economic metrics of interest (yield per batch, yield on methanol, unit production cost, and net present value). Sensitivity analysis reveals the dependence of techno-economic feasibility to R&D success, while uncertainty analysis quantifies how uncertainty in process development propagates into uncertainty in process performance.

Value-added Finance Use Animal Waste as Conversion Fertilizer for Vegetable Farming Groups in the West Sinjai Region, Indonesia

Agriculture plays a crucial role globally, and addressing the sustainable management of livestock waste is a growing concern. Converting animal waste into fertiliser not only supports organic farming but also offers a sustainable solution for managing livestock waste. This study aims to evaluate the conversion of fertiliser to animal waste and its potential to generate higher economic value for vegetable farmers in the West Sinjai region. By applying theoretical concepts learned in academic settings to real-world problems, this research aims to enhance knowledge regarding the economic benefits of fertiliser conversion. Additionally, it serves as a valuable reference for future studies on the economic impact of using animal waste as a substitute for chemical fertilisers. The primary goal is to determine the financial benefits and added value of using animal waste in place of chemical fertilisers. This research follows a quantitative descriptive approach, using both primary and secondary data. The population consists of secondary crop farmers in the West Sinjai region, and the sample includes six groups of vegetable farmers in Gunung Perak Village. The results indicate that converting fertiliser to animal waste significantly enhances the economic value for farmers in the West Sinjai region, particularly in Gunung Perak Village. This improvement is due to the cost savings from using animal waste compared to chemical fertilisers per production unit, ultimately leading to higher profits for the farmers.

Analysis of apple chips business development strategies using business model canvas approach and SOAR-AHP method

The study focuses on SMEs X, a producer of various processed fruit products, primarily apple chips. The business experienced a 90% decline in revenue due to an 8-month market closure during the Covid-19 pandemic, with ongoing challenges such as reduced revenue and production capacity post-pandemic. This study aims to identify business models in SMEs using the Business Model Canvas (BMC) approach, develop alternative business strategies through SOAR analysis of BMC elements, prioritize these strategies using the Analytical Hierarchy Process (AHP) method, and enhance their BMC. Four expert respondents participated in the study. The research results highlight the current business conditions across the 9 BMC elements. The SOAR matrix produced eight alternative strategies, prioritized using the AHP method, including optimal production planning and scheduling (OR2), strengthening apple chips brand awareness (OA2), disseminating freeze-drying production technology innovations (OA1), developing business unit production (SA2), expanding the marketplace (SA1), maximizing the use of social media (OR1), product quality standardization (SR1), and forming partnerships with other fruit chip SMEs to innovate freeze-dried products (SR2). The BMC was further developed by integrating SR2 and OA1 into the key partnerships element, SA2 into the key resources element, OA2, OR1, and OA2 into the key activities element, SR1 into the value proposition element, and SA1 into the channel element.

Assessing Queen Cell Cups Priming from Combs of Different Honey Bee Species for Mass Rearing of Apis mellifera L. Queen Bee

Experiments were carried out at the Beekeeping-cum-Honey Production Unit, Department of Entomology, Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India, during 2020-21. The study was aimed to evaluate queen cell cups prepared from combs of different bee species to find out a suitable technique for royal jelly production through mass queen bee rearing of Apis mellifera species. The worker bees of Apis mellifera (AM) exhibited a low preference for working with combs produced by Apis dorsata (AD) and Apis cerana (AC) for queen cell construction amongst evaluated combs. In contrast, when queen cell cups were made using AM combs, the worker bees attempted to build 100% of the cells. The time required by AM worker bees to complete queen cell construction on AM combs ranged from 7 to 15 days, however, they took 1.20 and 0.98 times longer to complete cells made with AD and AC combs, respectively. Similarly, queen bees showed reluctance to lay eggs in cells built on AD and AC combs. In queen cells constructed on AM combs, the queen bee began laying eggs on the second day after release into the nucleus hive and continued for five days, with an increasing trend in egg-laying activity. Among the different batches of eggs laid, the third batch was the most successful in respect of the larval growth and development. Additionally, larval growth showed a consistent upward trend during the first three days of observation for each laying batch. A. mellifera combs have been found to be more favourable for queen cell construction by worker bees, showing a strong preference for the queen's egg-laying performance, as well as optimal larval growth and development. Based on these findings, it is recommended that beekeepers may adopt this method to promote mass queen bee rearing and enhance royal jelly production.

Measurement and optimization method for aero-engine rotors based on binocular multi-line laser sensing and virtual assembly

During the assembly of aero-engine rotors, eccentricity and tilt errors along the radial and axial direction result in uneven loading along the mating surface, which significantly impacts the operational lifespan of the rotor. This paper proposes a three-dimensional contour measurement system based on binocular multi-line laser sensing and conducts an aircraft engine rotor stacking virtual assembly technology system, to response the practical requirements of aircraft engine rotor unit production assembly engineering. We combine multi-line structured light active projection device with the binocular stereo vision model to construct the contour measurement system, which enhances the surface features of the projected object by adhering to the common constraint of structured line and stereo vision. Furthermore, the error propagation model for multi-stage rotor assembly to investigate the mechanism of error impact is established aiming to facilitate quantitative research on virtual assembly mechanisms. By manipulating the rotor phases and evaluating the coaxiality under various conditions, effective prediction of phase variation can be achieved, the multi-objective benchmark virtual assembly method based on the error propagation model of multi-stage rotor coaxiality is presented. The experimental results indicate that the proposed measurement system achieves the maximum disparity between the spatial data points procured by the proposed measurement system and the predetermined value of 2.1 μm, and the maximum disparity between the target plane’s average movement distance and the preset value of 3.7 μm. The precision provided by this system effectively meets the objective of assembly phase optimization for multi-stage rotors. In the empirical experiment conducted on the three-stage rotors, both systems yielded identical optimized phases: 0° for the first-stage rotor, 270° for the second-stage rotor, and 120° for the third-stage rotor, thereby validating the efficacy of the proposed measurement and optimization method for aero-engine multi-stage rotors with virtual assembly.

The Management System of Dairy Buffalo in Peninsular Malaysia

Background: In Malaysia, there has not been enough the research on the cost of producing buffalo milk and the in different management system and insight into the industry’s current state is inadequate. This study aims to determine the cost of dairy buffalo milk in selected regions of Peninsular Malaysia Methods: A survey was conducted on 14 dairy buffalo farms from selected regions in Peninsular Malaysia: Selangor (six farms), Kedah (four farms), Penang (three farms) and Pahang (one farm) using a developed questionnaire. The questionnaire consists of farm management aspects like the socio-economics of the farmers, nutrition management, health management, dairy production management and the calculation of the production cost. The collected data were edited in Excel and analysed descriptively using IBM SPSS Statistics. Result: Based on the surveyed, The net profit was RM79,444.56, RM1,924,24.82 and RM2,220,519.02 in traditional, semi-commercial and commercial farms, respectively. This study reveals that milk costs are lowest at commercial farms, indicating the efficiency of farms. When more output is produced, the cost per production unit is reduced. Hence, the government should initiate and encourage the farmers to expand their farm business from traditional to commercial business and, at the same time, produce more skilful farmers.

Life cycle and techno-economic analyses of biofuels production via anaerobic digestion and amine scrubbing CO2 capture

The global energy landscape highlights the importance of the hydrogen economy, emphasizing its critical role in worldwide energy policies. This study explores a system designed for producing biomethanol and biomethane by integrating anaerobic digestion, biogas upgrading, and high-temperature electrolysis. The system builds on real-scale industrial data from an existing anaerobic digestion plant, which has been expanded to include biogas upgrading, an oxy-fuel gas turbine, a Solid Oxide Electrolysis Cell (SOEC), and a methanol production unit. Hydrogen, generated through electrolysis, synthesizes biomethanol by reacting with CO2. Additionally, the system produces biomethane through biogas upgrading. The system incorporates thermal energy integration with an oxy-fuel gas turbine. Life cycle assessment (LCA) results demonstrate the system’s environmental potential, achieved negative CO2 emissions of −0.0075 kgCO2eq/kgBiomass in case of photovoltaic panels and −0.0096 kgCO2eq/kgBiomass in case of wind turbines as electricity sources, attributed to efficient conversion of sewage sludge into valuable biofuels. Thermodynamic modeling in Aspen Plus shows an energy efficiency of 58.09 %, with outputs of 188 kg/h of biomethane and 269 kg/h of biomethanol. The techno-economic analysis reveals a payback period of 6.11 years and a levelized cost of biomethanol of 294.37 € per ton, indicating the system’s economic viability. The LCA further underscores the system’s sustainability, supporting its environmental benefits. This comprehensive analysis provides valuable insights into the viability and environmental impact of the proposed biofuel production system.

Modeling of an advanced renewable energy system coupled with hydrogen production and liquefaction for sustainable communities

The primary objective of this study is to develop a sustainable and efficient renewable energy-based multigeneration system that is specifically designed to meet diverse energy demands, such as heat, cooling, electricity and hydrogen. The system comprises several subsystems: a digester for biomass processing, a solar energy collection system, a thermal energy storage system, a multistage Brayton cycle, a steam Rankine cycle with reheat, a double-effect absorption system, a sonic hydrogen production unit, and a multistage hydrogen liquefaction system. The methodology involves conducting energy and exergy analyses, economic evaluation, and environmental impact assessment to determine the system's performance and viability. The energy efficiency results show that the Brayton cycle, steam Rankine cycle and liquefaction system have efficiencies of 30.92 %, 30.18 %, and 64.53 %, respectively. The exergy efficiencies for these subsystems are 50.10 %, 67.21 %, and 11.29 %, respectively. The double-effect absorption system exhibits energetic and exergetic coefficients of performance of 1.68 and 0.64, respectively. The overall energy and exergy efficiencies of the system are 36.41 % and 55.74 %, respectively. The economic analysis indicates a significant revenue potential, with the project reaching its break-even point in 2031 and achieving a net present value of $6.60 million. The results of an environmental impact assessment study highlight a reduction in CO2 emissions compared to conventional systems, emphasizing the system's contribution to sustainable energy solutions.

Rare yet relevant: Trondheim Cooperative Housing Association

ABSTRACT The paper explores the history of the housing cooperative Trondhjems Kooperative Boligselskap (TKB) [The Trondheim Cooperative Housing Association] in relation to societal and political currents in contemporary Norway. The article also discusses how the neighbourhood and its architectural layout were adapted to changing uses over a 100-year period. Established in 1922, the TKB is the second-oldest housing cooperative in Norway. An important distinction from similar cooperatives was its construction process, where members of the construction workers’ union built their own homes and set up a production unit to continue developing new projects. Two related movements contributed to the TKBB’s foundation: Firstly, the work of the Norwegian Association of Housing Reforms, and the Own Homes movement, both inspired by the Ebenezer Howard Garden City concept. Secondly, the labour movement, which initiated the construction of the TKB. The main actor behind the housing association was the Norwegian architect and planner, Sverre Pedersen. Consisting of 86 units in 11 duplexes and 16 quadruplexes, the timber houses are organized with small gardens and within larger green communal spaces. The article elaborates on how elements from the TKB’s organizational and architectural principles are relevant to developing social housing in Norway today.

Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future

The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.

Deep contextual reinforcement learning algorithm for scalable power scheduling

This article introduces a deep contextual reinforcement learning (DCRL) optimization algorithm to tackle the NP-hard power scheduling problem. The algorithm is based on a multi-agent simulation environment that decomposes the power scheduling problem into sequential Markov decision processes (MDPs). The simulation environment inherently corrects incorrect decisions and adjusts supply capacities so that the MDPs adhere to the optimization constraints. A deep reinforcement learning (DRL) model is trained on these MDPs to provide optimal solutions. Demonstrating its applicability and effectiveness, the proposed method is evaluated on various test systems with different unit numbers, constraints, and production cost functions. The experimental results show that the proposed method has better performance relative to alternative methods, such as binary alternative moth-flame optimization (BAMFO), binary particle swarm optimization (BPSO), teaching learning-based optimization (TLBO), new binary particle swarm optimization (NBPSO), binary learning particle swarm optimization (BLPSO), quasi-oppositional teaching learning-based algorithm (QOTLBO), binary-real-coded genetic algorithm (BRCGA), hybrid genetic-imperialist competitive algorithm (HGICA), three-stage priority list (TSPL), and hybridized evolutionary algorithm and sequential quadratic programming (HEPSQP). The novelties of the proposed method lie in its adaptability to longer planning horizons and linear dimensionality complexity, rendering it suitable for large-scale power systems.

Research on Production Operation System of Sucker Rod Pumping Units

Oil is the most important raw material in modern industry, and its extraction mainly relies on natural flow and artificial lifting. Sucker rod pumping is currently the most widely used method of artificial lifting for mechanical oil extraction, accounting for about 60% to 70% of all methods. This paper deeply explores the theoretical methods involved in the production operation process of sucker rod pumping units. From the perspective of sucker rod pumping unit production operation, a literature review is conducted on the flow patterns in the wellbore, pump efficiency analysis, and intermittent oil extraction systems, clarifying the optimization objectives of liquid production and system efficiency, and sorting out from the aspects of dynamic liquid level, pump efficiency, reasonable submergence depth, and intermittent operation systems. Through in-depth analysis of these key areas, a solid methodological foundation is provided for the future design and development of sucker rod pumping unit production operation systems.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Intelligent mix design of steel fiber reinforced concrete using a particle swarm algorithm based on a multi-objective optimization model

Steel fiber reinforced concrete (SFRC) is widely used in construction and is important for concrete-based applications. Nevertheless, compared with conventional concrete, it is difficult to efficiently and accurately design SFRC with a given mix ratio, because many factors affect the SFRC performance. This study proposes a multi-objective optimization model using numerical simulations and artificial intelligence to effectively derive the optimal SFRC mix proportion. For a quick and accurate relationship between the SFRC mix ratio and compressive strength, Latin hypercube sampling was used for sampling the random variables determining the mix ratio, yielding a set of random numbers. Subsequently, a finite-element simulation dataset was constructed, and a backpropagation neural network (BPNN) was used for predicting the complex nonlinear relationship between the raw material mix proportions and uniaxial compressive strength (UCS). The BPNN model was then utilized as the objective function in the multi-objective particle swarm optimization model, with the mix ratio parameters as the input variables and the compressive strength, unit production cost, and carbon dioxide emission as objective functions, which facilitated the search for the optimal mix proportion, yielding a Pareto-optimal solution set. Finally, based on the engineering preferences, the best solution was determined to be the recommended mix proportion.

Coupling a thermoelectric-based heat recovery and hydrogen production unit with a SOFC-powered multi-generation structure; an in-depth economic machine learning-driven analysis

This study presents a comprehensive technical, environmental, and economic analysis of a thermal power plant utilizing solid oxide fuel cells (SOFC) to meet urban demands for electrical power, fresh water, and hydrogen. The integrated system includes SOFC with anode and cathode recycling, multi-effect desalination, a power generation cycle with a heat recovery unit using a thermoelectric generator, and a hydrogen compression unit. A detailed parametric analysis was conducted to identify optimal conditions for key outputs such as total cost rate and exergy efficiency, employing genetic algorithms and artificial neural networks. According to the economic evaluation, the SOFC stack accounts for 65.11 % of total costs at 139.8 $/h, with the inverter contributing 11.9 %. The environmental analysis shows that the proposed system emits the least CO2 per energy unit compared to SOFC/GT and SOFC/GT/RC systems. The parametric study indicates that increasing the pressure ratio enhances the output power of the SOFC and the production of the gas turbine. However, this also leads to higher compressor consumption, thereby reducing net power. Furthermore, increasing the current density results in greater production of electricity, hydrogen, and freshwater, while also raising the exhaust gas temperature, which aids in the desalination process. The optimization results show an exergy efficiency of 61.38 % and a total cost rate of 132.9 $/h, with artificial neural networks reducing optimization time from 124 h to 14 min.

Calm Before the Storm?

Atlantic hurricane season poses an annual threat to the billions of dollars of oil and gas hardware pumping hydrocarbons from the depths of the US Gulf of Mexico (GOM). The region’s offshore production amounts to about 1.8 million B/D—roughly 14% of the total crude output in the US, according to the US Energy Information Administration, so any disruption is felt by oil markets across the globe. Prior to the 2024 hurricane season, forecasters pegged this year as primed to be particularly intense. Colorado State University (CSU), known for its storm prognostication, estimated that the basin would experience 23 named storms, with a dozen of those becoming hurricanes (winds of 74 mph or higher), and six of those strengthening to major hurricanes (winds of 111 mph or higher). Similarly, AccuWeather meteorologists expect between 20 and 25 named storms with a possibility of 30 or more. As the season enters its busy period, the basin has only experienced five named storms to date; however, many forecasters are sticking to their initial forecasts and continue to warn of an intense September/October period. “The months of September and October should be very active across the Atlantic,” said AccuWeather lead hurricane expert Alex DaSilva. “We continue to remain very concerned about rapid intensification. Sea-surface temperatures and ocean heat content are near record levels across most of the Atlantic basin. This can act like jet fuel for tropical systems. We also continue to believe that La Niña will emerge sometime in November. When water temperatures near the equator of the eastern Pacific Ocean are lower than the long-term historical averages for an extended period, La Niña is declared. Although this is a phenomenon in the Pacific, it has ramifications for weather patterns over the Atlantic Ocean. Specifically, La Niña can reduce the wind shear across the basin. When wind shear is lower, it makes it easier for tropical systems to take shape and strengthen.” The GOM has experienced one disruption in 2024 courtesy of Hurricane Beryl. Once a Category 5 storm (winds 155 mph and higher)—and the earliest-forming Category 5 storm on record—Beryl weakened as it crossed the southern Yucatán. The storm regained Category 1 strength as it crossed the far western offshore oil patch enroute to landfall near Matagorda Bay, Texas. Shell shut in production at its Perdido spar because of the storm as well as evacuated personnel from it and its Whale facility. Chevron moved all non-essential personnel off its Anchor floating production unit, which was undergoing commissioning at the time. The last major storm to cut a path straight through the heart of the US offshore oil patch was 2021’s Hurricane Ida. Ida tore through the region as a Category 4 storm before slamming into the Louisiana coast near Port Fourchon packing 150 mph winds on 29 August. The storm prompted the shut in of almost 100% of the Gulf’s oil and gas production and caused extensive damage to major hubs in the region, including the Shell-operated West Delta Block 143 complex. The facilities, which serve as the transfer station for production from Shell’s robust Mars corridor, were off line for several months.

Using CAD and CAD/CAM systems to prepare unit production for a CNC machine tool

Using CAD and CAD/CAM systems to prepare unit production for a CNC machine tool

75% Reduction in Packing Variation of in-House Central Sterile Supply Unit Products in a Restructured Hospital in Singapore

75% Reduction in Packing Variation of in-House Central Sterile Supply Unit Products in a Restructured Hospital in Singapore