Maximum Cost Savings Research Articles

P-1678. Post Implementation Analysis of Inpatient Review by Infectious Diseases Pharmacists of Outpatient Parenteral Antimicrobial Therapy (OPAT) Prior to Discharge

Abstract Background The Infectious Diseases Society of America OPAT guidelines recommend all patients should have ID expert review prior to initiation of OPAT. Prior to 2020, no formal OPAT program existed at this hospital system and prescribing was not limited to any specialty. However, in February 2020, the Outpatient Parenteral Antimicrobial Therapy - Inpatient Review (OPAIR) program was created in response to pre-implementation data showing only 16% of patients were discharged on appropriate therapy per IDSA OPAT guidelines. Figure 1. Outpatient Parenteral Antimicrobial Therapy Inpatient Review (OPAIR) program process Methods This retrospective, observational chart review identified patients for data collection if they had pharmacist documentation of OPAIR review during their index hospital stay. Patients were included in the pre-implementation group if prescribed OPAT and admitted between January 1, 2017, and December 31, 2017, in the health system. Patients were included in the post-implementation group if 18 years and older, prescribed OPAT, and admitted between February 2, 2020, to August 1, 2023, at Abbott Northwestern, New Ulm, Owatonna, Faribault, or River Falls Hospitals. Exclusion criteria include OPAT prescribed by an Infectious Disease (ID) provider or prior to index hospitalization. Chi squared tests were performed for all outcomes. Figure 2. Primary outcome of OPAT appropriateness. Appropriateness defined as a composite outcome: appropriate monitoring or follow up​, indication​, antibiotic selection​, antibiotic dose/frequency/duration, and recommended laboratory monitoring. Results There were 101 patients included in the pre and 79 in the post-implementation analysis. Internal Medicine was the most common prescriber in both the pre and post-implementation groups (60% vs 67%). OPAT appropriateness was significantly higher in the post-implementation group than in the pre-implementation group (52% (n=41/79) vs 16% (n=16/101), p< 0.001). The most common cause of inappropriate OPAT prescribing was a lack of monitoring or follow up (n=8/79). Complication rates were similar between pre and post groups (12% vs 18%, p=0.19) as was the 30-day readmission rate (17% vs 23%, p=0.32). Actual cost savings totaled $33,600 and maximum potential cost savings totaled to $214,200. The rates of midline utilization increased by 62% (p< 0.001). Figure 3. Secondary outcome of complication rate. Conclusion The OPAIR program at this health system significantly increased non-ID provider OPAT prescribing appropriateness. These results support the creation of a new workflow and expansion of the OPAIR program. Figure 4. Secondary outcome of cost avoidance. Disclosures All Authors: No reported disclosures

EVALUATION OF ENERGY SAVING EFFICIENCY OF BOILER COMBINED WITH HEAT PUMP FOR SUPPLEMENTARY FEED WATER HEATING

This paper investigates the integration of heat pumps for heating boiler feedwater and evaluates the impacts of feedwater temperature and condensate recovery rates on fuel consumption, energy efficiency, and CO2 emissions. The results show that using heat pumps significantly reduces boiler fuel consumption, especially when the feedwater temperature increases and the condensate recovery rate is high. The COP (Coefficient of Performance) of the heat pump gradually decreases as the water temperature rises; however, heating the water at lower temperatures yields better economic and energy efficiency. Economically, heat pumps provide substantial benefits, with maximum cost savings achieved at a water temperature of 75°C. Additionally, integrating heat pumps reduces CO2 emissions, particularly in boilers without condensate recovery, with the highest emission reduction reaching 17.8 kgCO2 per ton of steam. These findings demonstrate that using heat pumps is not only energy-efficient and cost-effective but also contributes to environmental protection by reducing greenhouse gas emissions.

Cooperative game theory based coordinated scheduling of production and transportation of parallel machines with deterioration effects

Abstract Cooperative game theory is used to study the production and transportation scheduling problem of parallel machines with deterioration effects. Jobs with an initial scheduling order are processed on parallel machines and then transported to customers. Penalties are incurred for early or late completion if the jobs are not completed on the due dates. Considering that jobs can rearrange the scheduling order in the coalition through cooperation to bring about cost savings, the cooperative game model is constructed by considering the jobs as players and taking the maximum cost savings of the coalition as the characteristic function. It is proven that when jobs have the same normal processing time and deterioration rate, the cooperative game has a non-empty core, and a core allocation can be obtained from the β-rule allocation method. A Dual Replay Buffer Double DQN (DRB-DDQN) algorithm is designed to get the optimal schedule of the coalition. Experiments demonstrate the effectiveness of the DRB-DDQN and β-rule allocation method in solving the game scheduling problem.

Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic

The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.

AI-optimization operation of biomass-based distributed generator for efficient radial distribution system

This research aims to optimize the size and location of biomass-based distributed generator (BMDG) units to enhance the voltage profile, reduce electrical losses, maximize cost savings, and decrease emissions from power distribution systems. Biomass-based distributed generator (BMDG) systems offer numerous advantages to enhance the efficiency of power distribution systems. However, achieving these benefits relies on determining the optimal size and position of the BMDGs. To achieve these objectives, the metaheuristic technique called particle swarm optimization (PSO) is employed to find the optimal placement and size of BMDGs. The proposed model was validated on MATLAB's IEEE-33 bus radial distribution system (RDS), confirming the aforementioned benefits. Comparative analysis between the PSO-based technique and other algorithms from previous research revealed better results with the proposed method. The results indicate that optimal placement and sizing of BMDG units have led to a reduction of more than 67.68% in active power losses and 65.90% in reactive power losses compared to the base case. Additionally, the reduction in active power loss was 40.44%, 11.39%, 42.85%, 1.81%, 0.85%, 29.83%, 5.82% and 28.38% more than artificial bee colony, backtracking search optimization algorithm, moth-flame optimization, Coordinate control, artificial Hummingbird algorithm, variable constants PSO (VCPSO), artificial gorilla troops optimizer (AGTO), and a jellyfish search optimizer respectively. Furthermore, the reactive power losses were reduced by 38.33% and 15.68% compared to VCPSO and AGTO respectively. Furthermore, this study revealed a cost reduction of 6.38% when compared to the AGTO and 1.30% when compared to the AHA. Moreover, the voltage profile of the power distribution system was improved by 7.28%. The presented methodology has demonstrated promising results for BMDGs in RDS across various applications.

The Role of Virtualization Technology to Increase Operational Cost Efficiency of Indonesian SMEs: Case Study of Internet Service Providers

This research investigates the role of virtualization technology in enhancing operational cost efficiency for Indonesian Small and Medium-sized Enterprises (SMEs), specifically Internet Service Providers (ISPs). A quantitative approach, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) version 2.0, was employed to analyze data collected from 68 respondents representing 216 Indonesian SME ISPs. The study focused on the relationship between virtualization technology planning, adaptation capability, server consolidation services, and operational cost effectiveness. The findings demonstrate that adaptability to virtualization technology is the most significant factor influencing operational cost efficiency. Effective implementation requires careful planning and a proactive approach to adapting to the technology's demands. Server consolidation services play a crucial role in optimizing resource utilization and reducing costs while virtualization technology offers substantial benefits, SMEs should carefully assess their specific needs and resources before implementation. Factors such as infrastructure, skills, and business objectives should be evaluated to ensure successful adoption and maximize cost savings. This research provides a foundation for further exploration into the impact of virtualization technology on SMEs. Future studies could investigate the applicability of these findings in different contexts, such as other industry sectors or regions. Additionally, examining the long-term effects of virtualization technology adoption and the potential for scalability could offer valuable insights. Further research could also focus on the challenges and opportunities associated with virtualization technology adoption in SMEs with varying sizes and resources.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

PCM as an energy flexibility asset: How design and operation can be optimized for heating in residential buildings?

In cold climates, residential buildings are often well-insulated to reduce heating costs but may lead to overheating. Incorporating Phase Change Material (PCM) into building envelope can serve as an effective measure to mitigate overheating and reduce cooling costs. However, the inappropriate incorporation of PCM in well-insulated building envelope might negatively affect the heating energy flexibility. Therefore, this paper explores the effectiveness of PCM in reducing heating costs by optimizing the PCM design and heating operation. The main objectives are to (i) quantify the performance of PCM in terms of overheating and cost savings, aligned with the recently introduced Time-of-Use (ToU) tariff and Spot pricing, (ii) identify the optimal design of PCM-incorporated building envelope, and (iii) apply a smart control with resetting temperature setpoints in-line with EN-ISO 52120-1 Standard. A multi-stage optimization process has been designed. It first identifies the optimal properties (i.e., melting temperature), position, and thickness of PCM. Secondly, it optimizes the profile of temperature setpoints, considering the seasonal thermal behavior of PCM. Co-simulation platform between IDA ICE 5.0 and Python 3.8 optimization engine is created for the heating control optimization. Results showed that PCM is more effective when incorporated into the interiors of external walls rather than in roofs. The optimal PCM design for Nordic buildings, featuring 75 mm-thick PCM with a melting temperature of 21 °C positioned within walls’ interior, has a varied impact on monthly energy savings across seasons, with a negative effect in spring and a significant positive effect in autumn. After the multi-stage optimization, the optimal scenario enables to achieve a maximum annual cost savings of 6.5 % with ToU tariff and 21.0 % with Spot pricing, while also eliminating overheating.

Ergonomic evaluation of assembly line work activity in the automotive industry: a case study

The automotive industry is growing worldwide with new technologies and seeking maximum cost savings. This sector is essential for the economic development of the country, with modern industries that invest in technological innovations and control of the activities of the production process. With this, the workstations become increasingly dynamic and characterized in the logic of productivity. Therefore, this study aimed to evaluate the work activity of operators of an assembly line of automotive components, from the ergonomic analysis. Through a descriptive and exploratory field study, the work activity of 30 operators of an assembly line of metallic components of the automotive industry was evaluated. Using instruments such as sociodemographic questionnaire; Nordic questionnaire; open interview, with collective questions about the development of the function, listed in an ergonomic demand items (EDIs) ranking; and complementary application of the NIOSH and OCRA methods. As a result, all respondents were male, with an average age of 30.13 ± 8.57 years, most had completed high school (76.7%), with an average working time in the company of almost two years (21.93 ± 12 months). Regarding relationships with coworkers, it was observed that the sector has a great relationship during the activities, with both colleagues and the boss. This study demonstrated a lower frequency of musculoskeletal symptoms in operators when compared to other studies. Symptoms that caused work leave were neck (3.3%), shoulders (3.3%), upper (6.7%) and lower back (6.7%), and hips/thighs (6.7%), ankle/foot (6.7%) were reported.

Intelligent decision modeling for optimizing railway cold chain service networks under uncertainty

Railway cold chain service network design (RCC-SND) aims to optimize the utilization of stations and lines as well as train allocations in a manner that minimizes costs while satisfying the service requirements of shippers. Furthermore, the uncertainties associated with freight demand, transportation costs, quality loss, station handling capacity, and arc capacity make the RCC-SND a complex decision-making problem. To tackle this challenge, we first formulate a Mixed-Integer Nonlinear Programming (MINLP) model to determine hub locations, freight wagon flows, and service frequency. To cope with uncertain parameters with varying degrees of uncertainty incorporated in the model, we extend the problem using fuzzy programming and further convert it to its crisp counterpart. A real-world cases study in Southwest China is performed to validate the proposed model, whose results provide different strategies for decision-makers with varying preferences. There are some main findings: As the number of hubs increases from 5 to 6, a maximum total cost savings of 1.99% can be achieved. Railway operators may opt for different decision preferences, for decisions prioritizing economic efficiency, the cost can decrease by 2.69% compared to deterministic optimization.

Feasibility assessment of behind-the-meter batteries under typical tariff structures for commercial and industrial customers

This paper presents a comprehensive analysis of cost savings and payback periods for the deployment of batteries by commercial and industrial customers, considering several typical tariff structures in the context of Australia. To achieve this, a battery energy management mechanism is proposed that enables battery charging, both from the solar photovoltaic system and the grid, and discharging precisely to maximize cost savings and minimize payback periods for commercial and industrial customers while minimizing battery degradation. The proposed mechanism directs the battery to charge from the grid during the most economical periods, and grid charging amounts are optimized based on the battery size and demand profiles, and to discharge during demand charge-, peak rate-, and shoulder rate-applicable periods in succession to reap maximum benefits. Furthermore, sensitivity analyses are conducted to showcase the economic viability of battery trial tariffs (tariffs introduced in Australia to expedite the uptake of batteries for commercial and industrial customers) compared to standard tariffs (usual tariffs imposed on commercial and industrial customers in Australia), considering various scenarios such as changes in battery sizes, customer profiles, and coulomb-ratings. The simulation results suggest that the battery trial tariff is more profitable for a sample commercial and industrial customer compared to the standard tariff, enabling the reduction of payback periods by between 1.53% and 38.05%, for example, under the Tariff 1 structure (one example of a tariff structure introduced by a typical service provider in Australia).

Load Scheduling of Smart Net-Zero Residential Buildings Based on Pandemic Situation

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.

Determination of Pipe Diameter Using Optimization Method

During the design phase of engineering networks, a critical issue remains the selection of pipe diameters that minimize capital investments for transporting the heat carrier from the heat source to the consumers. Object of study: a pressurized pipeline of circular cross-section with a moving heat carrier. Subject of study: total monetary costs for transporting the heat carrier as a function of the chosen pipeline diameter. Objective of study: to determine the pipe diameter that achieves maximum cost savings for transporting the heat carrier under given design conditions. Research methods: theory of hydraulic calculation for circular cross-section pipelines and theory of heat transfer through a single-layer cylindrical wall under steady-state conditions. Research results: using an example from a centralized heating system pipeline section, it was established that for a mass flow rate of the heat carrier equal to 32.9 t/h, the optimal pipe size would be 76×3 mm (nominal diameter 70 mm). Under current tariffs for thermal energy (24.82 USD/Gcal) and electrical energy (6.65 USD/(MW⋅h)), the total monetary costs for transporting the heat carrier over a heating season would be 56.28 USD per 1 running meter of pipeline. In comparison, with a nominal diameter of 50 mm, the total costs amounted to 90.37 USD; with a diameter of 80 mm, the costs were 63.29 USD. The developed method for hydraulic design calculations is universal and can be applied in the design of engineering networks where the working medium is a moving heat carrier (steam or hot water).

Enhancing energy efficiency and comfort with a multi-domain approach: Development of a novel human thermoregulatory model for occupant-centric control

Occupant Centric Control (OCC) strategies aims to achieve a more personalized and comfortable indoor environment, translating occupants' comfort requirements into real environmental conditions. This strategy relies on a multilevel non-linear programming optimization procedure to determine optimal thermo-hygrometric setpoints. The objective is to minimize space heating and cooling requirements while addressing multi-domain comfort concerns related to individual thermal sensations and perceived air quality. To facilitate this process, this study adopts a novel Personal Comfort Model (PCM), incorporating physiological factors to predict occupants' thermal sensations and CO2 intakes. The PCM's reliable predictive capabilities are confirmed through validation with real experimental data from a test room at the University of Perugia. For detailed energy analyses, considering occupants' subjective comfort preferences, the PCM is seamlessly integrated into DETECt, an in-house building energy performance simulation tool developed by the University of Naples Federico II, for the design of advanced control algorithms and energy efficiency strategies. To effectively manage the HVAC system, a model predictive control is implemented, using the determined setpoints to ensure mechanical ventilation and maximize cost savings. The proof of concept for the developed methodology involves simulating experimental tests using the thermal model of the human body and the new facility management system, to be simulated and optimized by means of the enhanced building energy performance simulation tool, exploited for the design and operation of more occupant-centric and sustainable buildings. The proposed study demonstrates that through the developed OCC strategy enables a significant reduction in thermal discomfort (40 % and 60 % less than the occupation time for test 1 and test 2, respectively) compared to reference scenarios. Additionally, energy analysis reveals high efficiency, achieving savings of 12.8 % and 7.8 % of electricity consumed for HVAC system operation.

A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing

Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics such as response time, throughput, and energy consumption. Resource optimization techniques play a crucial role in achieving these objectives. Resource optimization techniques aim to efficiently allocate resources to jobs, taking into account factors like resource availability, job priorities, and constraints. These techniques utilize various algorithms and optimization approaches to make intelligent decisions about resource allocation. Research on resource optimization techniques for job scheduling in cloud computing is of significant importance due to the following reasons: Efficient Resource Utilization: Cloud computing environments consist of a large number of resources that need to be utilized effectively to maximize cost savings and overall system performance. By optimizing job scheduling, researchers can develop algorithms and techniques that ensure efficient utilization of resources, leading to improved productivity and reduced costs. Performance Improvement: Job scheduling plays a crucial role in meeting performance metrics such as response time, throughput, and reliability. By designing intelligent scheduling algorithms, researchers can improve the overall system performance, leading to better user experience and customer satisfaction. Scalability: Cloud computing environments are highly scalable, allowing users to dynamically scale resources based on their needs. Effective job scheduling techniques enable efficient resource allocation and scaling, ensuring that the system can handle varying workloads without compromising performance. Energy Efficiency: Cloud data centres consume significant amounts of energy, and optimizing resource allocation can contribute to energy conservation. By scheduling jobs intelligently, researchers can reduce energy consumption, leading to environmental benefits and cost savings for cloud service providers. Quality of Service (QoS): Cloud computing service providers often have service-level agreements (SLAs) that define the QoS requirements expected by users. Resource optimization techniques for job scheduling can help meet these SLAs by ensuring that jobs are allocated resources in a timely manner, meeting performance guarantees, and maintaining high service availability. Here in this research, we have used the method of the weighted product model (WPM). For the topic of Resource Optimization Techniques Based Job Scheduling in Cloud Computing For calculating the values of alternative and evaluation parameters. A variation of the WSM called the weighted product method (WPM) has been proposed to address some of the weaknesses of The WSM that came before it. The main distinction is that the multiplication is being used in place of additional. The terms "scoring methods" are frequently used to describe WSM and WPM Execution time on Virtual machine, Transmission time (delay)on Virtual machine, Processing cost of a task on virtual machine resource optimization techniques based on job scheduling play a crucial role in maximizing the efficiency and performance of cloud computing systems. By effectively managing and allocating resources, these techniques help minimize costs, reduce energy consumption, and improve overall system throughput. One of the key findings is that intelligent job scheduling algorithms, such as genetic algorithms, ant colony optimization,

Technology Focus: Flow Assurance (November 2023)

This year has seen a focus on gas development projects and the energy transition. Flow assurance plays an interesting role in this area. Even though the attention has been on the energy transition, where gas development is concerned, the production processes through which gas is produced cannot be ignored. Thus, flow-assurance issues remain prevalent today, and an analysis of existing solutions, key to the success of oil and gas producing facilities, needs to be addressed. Tackling mixed-scale issues in the oil field using a novel robust scale dissolver (RSD) was studied in paper SPE 211187. Scaling, an incompatible-fluids-related flow-assurance problem in oil and gas wells at various locations in the Malaysian basin, results in rapid oil production decline. RSD is said to be capable of dissolving up to 100% of mixed scales in 24 hours at well temperatures with no incompatibility with production chemicals, pumping, and wireline components. This was done under laboratory conditions. A field trial was completed, with outstanding results for the RSD as it proved capable of reviving the well by resolving mixed-scale issues. Paper SPE 211943 discusses a thermodynamic modeling approach for prediction and prevention of wax deposition. Wax deposition, an oilfield problem prevalent with aging wells, requires a degree of accuracy with prediction to have better control over prevention approaches. The paper discusses the thermodynamic modeling approach used to determine the wax appearance temperature in a well. This tool also confirmed the optimal depth at which to place a wax-inhibition tool based on the life-cycle expectations of the well. This improved well availability by 30%. Paper OTC 32170 addresses performance mapping of a multiphase-flow model. Exploration-data-analysis techniques were used that enabled a comprehensive analysis of several independent data sets from various origins. The analyses provided actionable and more-general insights that would have been otherwise obscured were individual data sets to be analyzed independently. These papers addressed prevailing issues critical for success and production optimization. Addressing the dissolution of mixed scale is a pressing need in modern operations without necessarily finding the scales in isolated conditions. Wax remediation relies highly on accurate temperature data for maximum efficiency and cost savings. I highly recommend reading the second highlighted paper to understand how the well availability of 30% was achieved. Data analytics in oil and gas enables actionable insights easily identified by the last reviewed paper. I trust, with these updates in flow assurance in recent times, we are well placed and assured of production-facility support for the coming years, contributing positively to the goals of the energy transition through fuels such as gas. Recommended additional reading at OnePetro: www.onepetro.org. SPE 213817 Multifunctional Flow Assurance Inhibitors: Three Birds With One Stone? by Malcolm A. Kelland, University of Stavanger, et al. SPE 215003 Significant Reduction of the Viscosity of Waxy Oils by Electrical Treatment by Hao Wang, The University of Texas at Austin, et al.

Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

Optimal Reactive Power Injection in Distribution Networks to Maximize Cost Savings Via AOA

Utilities have been forced to raise the overall efficiency towards a better position in radial distribution systems (DS). The literature has proved that reactive power compensation performs well in minimizing the power loss ( PLoss ) and enhancing the bus voltage profile within the permissible range in radial DSs. This work presents Archimedes optimization algorithm (AOA) to resolve the problem efficiently. The merit of this technique is that it can offer a global or near-global optimum for capacitor siting and sizing. The main intention of this study is to obtain annual financial benefit (AFB) using the placement and sizing of capacitors optimally. This can, however, be achieved by minimizing the objective function composed of cost-based PLoss and capacitor investment cost on radial DSs. The proposed technique has been tested on four renowned DS: the Indian 10-bus, modified 12-bus, IEEE 69-bus, and 94-bus Portugal system. The previously published papers are compared with the outcomes of AOA in terms of PLoss reduction and AFB and prove that AOA achieves better performance and more AFB.

Solar-assisted hybrid oil heating system for heavy refinery products storage

The purpose of this study is to investigate the potential use of solar energy within an oil refinery to reduce its fossil fuel consumption and greenhouse gas emissions. A validated ASPEN HYSYS model was used to investigate the products produced from heavy crude oil in the refinery. Using TRNSYS software, the proposed Parabolic Trough Collector (PTC)-based solar heating system paired with the boiler is modelled. Sensible thermal energy storage (TES) system is integrated into the refinery's process heating to handle the intermittent nature of solar energy. It was discovered that 463 m2 of the PTC area coupled with a 15000-L TES tank can result in a maximum life cycle cost savings of 21.046 thousand USD for an annual heat supply of 116,944 MWh to generate steam at a temperature of 200–220 °C. In the proposed hybrid heating system, the yearly solar fraction is determined to be 26.99% and the payback period is 8.77 years with the average solar irradiance of 900 W/m2. In addition, the system can yearly reduce greenhouse gas (GHG) emissions by about 34.045 tonnes of CO2 equivalents.

Optimal Solution of Peer-to-Peer and Peer-to-Grid Trading Strategy Sharing between Prosumers with Grid-Connected Photovoltaic/Wind Turbine/Battery Storage Systems

This paper presents an energy management peer-to-peer (P2P) and peer-to-grid (P2G) trading strategy for power sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. The optimal results are obtained using the “Fmincon” solver due to its ability to solve large-scale optimization problems. Two prosumers sectors are considered in this study that is the residential and commercial. The results show that for the residential consumer side, the load is satisfied by the energy from the grid, its own renewable energy system (RES), and the energy from the commercial consumer side RES during the first off-peak period (00:00 AM to 05:00 AM). During the peak pricing period (17:00 PM to 22:00 PM), the load is satisfied by the residential consumer’s battery and the commercial consumer’s battery, and no energy is bought from the grid due to the high price. Furthermore, 37.69% and 39.40% cost savings for residential and commercial, respectively, and a reduction of the bill by $42.26/day and $42.97154/day, respectively, are recorded. Significant grid energy savings for the developed P2P/P2G trading for prosumers and maximum daily cost savings are achieved compared to existing cases such as P2G only and P2P only. Finally, the residential consumer can achieve a greenhouse gases (GHG) emissions reduction of 141.88 kgCO2-eq and 176.99 kgCO2-eq net savings, while the commercial consumer can achieve 122.34 kgCO2-eq GHG emissions mitigation and 196.53 kgCO2-eq net savings.