Operational Flexibility Research Articles

Role of Ship Guidance in Reducing Accident Risks in Tanjung Perak’s Mandatory Waters

This study investigates the effectiveness of ship guidance in minimizing accident risks within the mandatory guidance waters of Tanjung Perak Surabaya. Employing qualitative research methods and descriptive analysis, the research evaluates ship pilot performance based on communication effectiveness, maneuvering precision, regulatory knowledge, emergency response, and professionalism. The findings reveal that effective pilotage significantly enhances navigational safety, contributing to a 20% reduction in accident rates. Despite generally high performance, challenges such as communication barriers, navigational complexity, and regulatory constraints persist. Recommendations include investing in advanced navigational technologies, improving communication protocols, updating regulations for better operational flexibility, and ensuring regular equipment maintenance. The study highlights the critical role of ship pilots in ensuring safe maritime operations and provides actionable insights for optimizing pilotage practices to further enhance safety in complex navigation environments

Energy saving scheduling of power and two steam loads for a CHP system consisting of multiple CHP units integrated with steam ejectors

Combined heat and power (CHP) can effectively enhance the energy efficiency of thermal power plants. However, the heat-dominated operation mode restricts the CHP unit's operation flexibility which is essential for the power grid for peak shaving. The double-extraction CHP unit, which cogenerates power and two-pressure steam for industrial or municipal heating users, is typical, complex, and widely applied. The integration of steam ejectors is an effective way to enhance the operational flexibility of the double-extraction CHP unit, which makes the scheduling of power and two steam loads very complicated. Therefore, the scheduling model of a CHP system consisting of multiple CHP units integrated with steam ejectors is developed in this study to achieve energy saving. A reference CHP system with four 1000 MW CHP units with steam ejectors is analyzed, and the operational flexibility and energy consumption characteristics of the units are analyzed. Then, the scheduling of power and two steam loads within a typical day is conducted. The results show that the coal consumption is reduced by 0.65–3.10 t h−1 compared with the original heating mode.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

An Intelligent Decision Support System for Spherical Fuzzy Sugeno-Weber Aggregation Operators and Real-Life Applications

This article presents a novel approach to a decision support system for handling uncertainty and impreciseness in a large amount of human opinion. Sometimes, the aggregation of real-life applications is quite complex due to incomplete and redundant information about different preferences or alternatives. To handle such type of situations, a spherical fuzzy environment is a more effective and feasible framework with four components membership, abstinence, non-membership and refusal degree. We also formulate some flexible operations of Sugeno-Weber aggregation operators. Motivated by the theory of Sugeno-Weber t-norms, we constructed a family of mathematical methodologies, including Sugeno-Weber weighted average and weighted geometric operators in the light of spherical fuzzy information. An appropriate decision-making technique of the multi-attribute decision making (MADM) problem is also demonstrated to resolve complicated real-life applications. A numerical example is used to verify the compatibility and effectiveness of discussed mathematical approaches.

Simulations of design and high load operation of a model Francis turbine using bridged approach to turbulence modelling

Abstract Off-design operation of hydraulic turbines is contemporarily frequented for balance of variable energy intermittence in the electric grid. Being operationally highly flexible, these turbines allow a quick transition to off-design operation from the design point. However, such operational flexibility, and therefore the grid balancing capability is impeded by generation of flow instabilities like vortex breakdown during off-design operation. Vortex breakdown causes losses in efficiency and pressure recovery, pressure fluctuations and possibly mechanical vibrations in event of resonance between system natural and flow field fluctuation frequencies. While substantial experimental and numerical effort has already been made to study draft tube vortex breakdown, an accurate numerical flow characterization of the phenomenon is still a challenge. To this end, operation of a high head model Francis turbine under design and high load regimes using a bridged turbulence modelling approach is simulated. The approach allows a seamless transition between direct numerical simulation and Reynolds averaged Navier-Stokes equations. The highest attainable accuracy is limited by the mesh size. As such a satisfactory compromise between desired accuracy and invested computational effort is attained. The flow in the draft tube is free of anomalies under design specified operation. However, at high load an axial flow stagnation occurs centrally, and the flow is separated about the stagnated zone. The core of the vortex is enlarged with flow recirculation within it. Shear layers between the central stagnant zone and surrounding outflow kink and roll up transforming it into a spiral structure. In this work, a basic yet accurate numerical flow characterization of the aforementioned flow situations is achieved.

AI-Based Prediction Module of Key Neutronic Characteristics to Optimize Loading Pattern for i-SMR with Flexible Operation

AI-Based Prediction Module of Key Neutronic Characteristics to Optimize Loading Pattern for i-SMR with Flexible Operation

Design and optimization of zero-carbon integrated energy system for highway service area

Abstract The challenge of achieving dual-carbon targets arises from the issue of high carbon emissions in Chinese highway service areas. To address this, a comprehensive electric-thermal-hydrogen energy system is proposed, consisting of a new energy generation system, heat pumps, electrolyzers, hydrogen fuel cells, energy storage devices, and charging stations. A dispatch model for the integrated energy system is established, with the objective of minimizing the total operating cost while satisfying the constraints of each device and the electric-thermal-hydrogen balance. The model is implemented in MATLAB, and the ideal optimal solution for each decision variable is obtained using the Gurobi solver. The investment and operating costs, as well as the scheduling results for electricity, heat, and cooling, are analyzed. The results indicate that the electrolyzer-fuel cell system can improve the system’s operational stability and flexibility in dispatch optimization. Due to the instability of photovoltaic and wind power devices and the fluctuating nature of loads, energy storage devices are required for peak load shaving in the system. The zero-carbon integrated energy system model can calculate the most economical investment plan based on the load and meteorological conditions of different service areas.

Development of Aczel-Alsina t-norm based linear Diophantine fuzzy aggregation operators and their applications in multi-criteria decision-making with unknown weight information

Aczel-Alsina t-norm and t-conorm are intrinsically flexible and endow Aczel-Alsina aggregation operators with greater versatility and robustness in the aggregation process than operators rooted in other t-norms and t-conorm families. Moreover, the linear Diophantine fuzzy set (LD-FS) is one of the resilient extensions of the fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PyFSs), and q-rung orthopair fuzzy sets (q-ROFSs), which has acquired prominence in decision analysis due to its exceptional efficacy in resolving ambiguous data. Keeping in view the advantages of both LD-FSs and Aczel-Alsina aggregation operators, this article aims to establish Aczel-Alsina operation rules for LD-FSs, such as Aczel-Alsina sum, Aczel-Alsina product, Aczel-Alsina scalar multiplication, and Aczel-Alsina exponentiation. Based on these operation rules, we expose the linear Diophantine fuzzy Aczel-Alsina weighted average (LDFAAWA) operator, and linear Diophantine fuzzy Aczel-Alsina weighted geometric (LDFAAWG) operator and scrutinize their distinctive characteristics and results. Additionally, based on these aggregation operators (AOs), a multi-criteria decision-making (MCDM) approach is designed and tested with a practical case study related to forecasting weather under an LD-FS setting. The developed model undergoes a comparative analysis with several prevailing approaches to demonstrate the superiority and accuracy of the proposed model. Besides, the influence of the parameter Λ on the ranking order is successfully highlighted.

Synthesis of flexible inter-plant heat exchanger networks: A decomposition method considering intermedium fluid circles

The traditional methods for synthesizing flexible heat exchanger networks (HENs) are not directly applicable to inter-plant HEN challenges, primarily due to the spread of system uncertainty across plants via intermedium fluid circles. This complicates the synthesis process significantly. To tackle this issue, this study proposes a decomposed stepwise methodology to facilitate the flexible synthesis of the inter-plant HENs performing indirect heat integration. A decomposition strategy is proposed to divide the overall network into manageable sub-networks by dissecting the intermedium fluid circles. To address the variability in intermedium fluid temperatures, a temperature fluctuation analysis approach is developed and a heuristic rule is introduced to maintain the temperature feasibility of the intermedium fluids. To ensure adequate flexibility and cost-effectiveness of the designed networks, flexibility analysis and network retrofit steps are conducted through model-based optimization techniques. The efficacy of the method is demonstrated through two case studies, showing its potential in achieving the desired operational flexibility for inter-plant HENs.

CONSTRUCTED WETLANDS FOR NITROGEN REMOVAL AND SIMULTANEOUS REUSE OF DOMESTIC SEWAGE

This study evaluated the behavior of vertical flow constructed wetland (VFCW), planted with irrigated rice (Oryza sativa L.), specie BRS-GO Guará, and a vertical flow soil filter without plant (VFSF-CTR), as control, both on a reduced experimental scale. The objective was the removal and recovery of N from domestic sewage, using soil latossoil red-yellow (LRY) mixed with medium sand (modified LRY) as support medium. The operation of VFCW and VFSF-CTR for ten months and two rice crop cycles, used three different hydraulic loading rates - HLR (4.0, 8.0 and 15.0 cm/d); with three repetitions, totalizing eighteen experimental units. For the VFCW, the total Kjeldahl nitrogen (KTN) and N-NH4+ removal efficiencies, ranged from 86% to 95%; and, for VFSF-CTR, from 79 to 94%; with the rice crop favoring higher efficiencies in KTN removal, independent of HLR. Both systems showed tertiary treatment removal efficiency in a single stage; in addition, the variations in the removal efficiencies were minimal when operated with HLR of 15 cm/d. There was great accumulation of KTN and mineral N (N-NH4+ and N-NO3-) in soil, mainly in the 0-5 cm soil layer, which can be used for fertilization of other crops. There was also N immobilised in microbial biomass (MBN). This rapid fertilisation improved the soil quality in a short term (ten months of sewage application) and provided the production of grains and vegetable biomass for human and animal food. The VFCW presented good results, either operated with low or high hydraulic loads, showing larger operational flexibility.

Demonstration-enhanced policy search for space multi-arm robot collaborative skill learning

The increasing complexity of on-orbit tasks imposes great demands on the flexible operation of space robotic arms, prompting the development of space robots from single-arm manipulation to multi-arm collaboration. In this paper, a combined approach of Learning from Demonstration (LfD) and Reinforcement Learning (RL) is proposed for space multi-arm collaborative skill learning. The combination effectively resolves the trade-off between learning efficiency and feasible solution in LfD, as well as the time-consuming pursuit of the optimal solution in RL. With the prior knowledge of LfD, space robotic arms can achieve efficient guided learning in high-dimensional state-action space. Specifically, an LfD approach with Probabilistic Movement Primitives (ProMP) is firstly utilized to encode and reproduce the demonstration actions, generating a distribution as the initialization of policy. Then in the RL stage, a Relative Entropy Policy Search (REPS) algorithm modified in continuous state-action space is employed for further policy improvement. More importantly, the learned behaviors can maintain and reflect the characteristics of demonstrations. In addition, a series of supplementary policy search mechanisms are designed to accelerate the exploration process. The effectiveness of the proposed method has been verified both theoretically and experimentally. Moreover, comparisons with state-of-the-art methods have confirmed the outperformance of the approach.

Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs

We study the elective surgery planning problem in a hospital with operating rooms shared by elective and emergency patients. This problem is split in two distinct phases. First, a subset of patients to be operated in the next planning period is selected and the selected patients are assigned to a block and a tentative starting time. Then, in the online phase of the problem, a policy decides how to insert the emergency patients in the schedule and may cancel planned surgeries. The overall goal is to minimize the expectation of a cost function representing the assignment of patient to blocks, case cancellations, overtime, waiting time and idle time. We model the offline problem by a two-stage stochastic program, and show that the optimal second-stage costs can be approximated by a convex piecewise linear surrogate model that can be computed in a preprocessing step. This results in a mixed integer program which can be solved very fast, even for large instances of the problem. We also describe a greedy policy for the online phase of the problem, and analyze the performance of our approach by comparing it to both heuristic methods or approaches relying on sampling average approximation (SAA) on a large set of benchmarking instances. Our simulations indicate that our approach can reduce the expected costs by as much as 30% compared to heuristic methods and it can solve problems with 1000 patients in about one minute, while SAA-approaches fail to obtain good solutions within 30 min on small instances.

Low‐carbon economic schedule of the H2DRI‐EAF steel plant integrated with a power‐to‐hydrogen system driven by blue hydrogen and green hydrogen

AbstractHydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production.

Effect of long-term storage of cattle manure on its energy potential and biodegradability

Abstract In recent years in Russia, due to the complicated geopolitical situation in some border areas, biogas plants have been considered not only as waste utilization facilities, but also as reserve energy sources, which are safer than traditional - nuclear power plants. In this context, the development of an algorithm for the flexible operation of a biogas plant is particularly relevant, which gives rise to the need to study the stability of the system under the influence of different unfavourable factors. In this work, the influence of long-term storage of cattle manure on its energy potential and biodegradability is studied. The specific methane yield in the test variant with manure stored for 10 months before anaerobic fermentation was 1.41±0.55 ml/g oDM, which is 6.23 times lower than in the test variant with fresh manure; the methane content of manure after long-term storage is 9.66 times lower and the degree of decomposition of its organic matter is 2.72 times lower compared to similar indicators of manure processed without preliminary storage. However, the specific biogas yield from long-term stored manure is 1.66 times higher than the control, which indicates intensive formation of other gases. Thus, long-term storage has a negative impact on the energy potential of cattle manure; if it is necessary to process it in a biogas plant, it is advisable to combine it with more energy-intensive raw materials.

Drone Warfare and Threshold of the Use of Force

The proliferation of drones among both state and non-state actors, as evidenced by their widespread use, signals a shift towards more accessible forms of military engagement. In this context, the paper discusses how drones, as unmanned, remotely operated aircraft, have revolutionized warfare by enabling remote engagement and minimizing personnel risk. Concepts such as risk compensation, the 'body bag syndrome', and the potential for drones to lower the threshold for use of force has also been explored. The precision and long endurance of drones, coupled with their cost-effectiveness, emerge as double-edged swords, enhancing military capabilities while raising questions about the ease of resorting to force. The paper highlights the operational advantages of drones over manned aircraft, including rapid deployment, reduced training requirements, and operational flexibility. This paper also explores the transformative role of drone technology in contemporary military operations and examines the impact of drones on the threshold of use of force in contemporary times. Following the thematic analysis of qualitative sources backed by quantitative data, this paper explores how drones have lowered the threshold for the use of force.

Research on motion planning system for wall-climbing mobile manipulator for large steel structures welding operation

Purpose The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility. Design/methodology/approach A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling. Findings Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions. Originality/value The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

An interactive framework for strategic participation of a price-maker energy hub in the local gas and power markets based on the MPEC method

Power-to-gas (P2G) technologies within smart energy hubs (SEH) offer unprecedented operational flexibility to distribution networks. This is achieved by integrating various energy carriers both regionally and locally, particularly in the presence of renewable energy sources (RES). According to this subject, the present work outlines an interactive risk-based model and meticulous mathematical solution to justifiably incorporate the P2G units into the SEH to participate in the local electricity and natural gas markets strategically. As a price-maker prosumer, the SEH utilizes cross-product arbitrage strategies to optimize its proposals for power and gas exchanges in the local markets. The participation model for SEH is developed as a bi-level framework. At the top level, the goal is to maximize SEH's profit while accounting for the risks posed by RES. At the bottom level, the aim is to increase social welfare under network constraints through local market settlements. Finally, a mathematical program with equilibrium constraints (MPEC) is offered as a solution to solve the bi-level model. Using integer disjunctions on the complementarity and slackness criteria and the strong duality theory on the bilinear product terms, a mixed-integer linear programming (MILP) has been derived to approximate the suggested MPEC formulation. It can be concluded from the results that the integration of P2G technologies and the use of mutual product arbitrage in the local electricity and natural gas markets, not only increases the profit of the SEH, but also reduces operating costs and improves social welfare as well as cost-recovery of the entire network.

Power supply characterization of baseload and flexible enhanced geothermal systems

We investigated the techno-economic feasibility and power supply potential of enhanced geothermal systems (EGS) across the contiguous United States using a new subsurface temperature model and detailed simulations of EGS project life cycle. Under business-as-usual scenarios and across depths of 1–7 kilometers, we estimated 82,945 GW and 0.65 GW of EGS supply capacity with lower levelized cost of electricity than conventional hydrothermal and solar photovoltaic projects, respectively. Considering the scenario of flexible geothermal dispatch via wellhead throttling and power plant bypass, these estimates climbed up to 184,112 GW and 44.66 GW, respectively. The majority of EGS supply potential was found in the Western and Southwestern regions of the United States, where California, Oregon, Nevada, Montana, and Texas had the greatest EGS capacity potential. With advanced drilling rates based on state-of-the-art implementations of recent EGS projects, we estimated an average improvement of 25.1% in the levelized cost of electricity. These findings underscored the pivotal role of flexible operations in enhancing the competitiveness and scalability of EGS as a dispatchable renewable energy source.

Lignin extraction in chemical pulp mills: The role of flexible operation

Lignin extracted from black liquor in chemical pulp mills can potentially replace fossil carbon feedstocks in fuels and materials, thereby increasing the economic and environmental added values of woody biomass. However, since lignin extraction reduces the electricity generation of the mill, the added value depends on the characteristics of the electricity market in which the mill operates. In this study, a model mill is exposed to two different electricity price profiles: the low and steady prices of south-central Sweden in Year 2019; and the high and volatile prices of the same region in Year 2022. For the model mill, investments in lignin extraction designed to increase pulp production are economically viable and have low levels of sensitivity to electricity price levels and price volatility. The viability of lignin extraction without increased pulp production depends on the relationship between the electricity and lignin prices. With stable electricity prices, or steady mill operation, a rule-of-thumb holds that for lignin extraction to be viable, the lignin price (€/t) must be 1.8-times the average electricity price (€/MWh) plus 40 €/t for the supply of chemicals. With volatile electricity prices and flexible operation of the recovery boiler, the mill can shift the loss in electricity sales to low-price hours, thereby saving 15–70 % of the operational costs of lignin extraction, as compared to steady operation. This effect can be further enhanced by increasing the capacity of the lignin extraction process or extending the size of the black liquor storage tank. The proposed flexibility measures allow the market-integrated pulp mill to export lignin to replace fossil carbon supplies in other sectors, while supporting the electricity system during hours with high demand and low supply.

The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.