Optimization Approach Research Articles

Stochastic Control of Battery Energy Storage System with Hybrid Dynamics

This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Numerical study of the mechanisms of nano-assisted foam flooding in porous media as an alternative to gas flooding

Oil reservoirs are nearing maturation, necessitating novel enhanced oil recovery (EOR) techniques to meet escalating global energy demands. This demand has spurred interest in reservoir production analysis and forecasting tools to enhance economic and technical efficiency. Accurate validation of these tools, known as simulators, using laboratory or field data is pivotal for precise reservoir productivity estimation. This study delves into the application of nanoparticles in foam flooding for mobility control to improve sweep efficiency. Foam generation can occur in-situ by simultaneous injection of surfactants and gas or through pre-generated foam injection into the reservoir. In this work, a series of systematic simulations were run to investigate how much injected fluids can reduce gas breakthrough while also increasing oil recovery. Subsequently, we analyzed the most effective optimization strategies, considering their economic limits. Our primary objective is to numerically model nanofoam flooding as an innovative EOR approach, synergizing foam flooding mechanisms with nanotechnology benefits. In this work, modeling of nanoparticles in foam liquid was represented by the interfacial properties provided to the injection fluid. Additionally, we simulated Water-Alternating-Gas (WAG) injection schemes across various cycles, comparing their outcomes. Our results showed that nanofoam injection achieved a higher recovery factor of at least 38% and 95% more than WAG and gas injections, respectively. The superior efficiency and productivity of foam injection compared to WAG and gas injection suggest an optimal EOR approach within the scope of our model. These simulated optimization techniques contribute to the future development of processes in this field.

The role of maintenance therapy following autologous stem cell transplantation in newly diagnosed multiple myeloma: Considerations on behalf of the Chronic Malignancies Working Party of the EBMT.

Recent treatment advancements in multiple myeloma have led to significant improvements in patient outcomes. Maintenance therapy following autologous haematopoietic stem cell transplantation (AHCT) is now standard of care and has been demonstrated to prolong and deepen treatment responses. Currently, lenalidomide remains the single agent that has been approved for maintenance post-AHCT in Europe and the USA which, if tolerated, is continued until disease progression. The treatment landscape is rapidly expanding however, and the optimal personalised maintenance approach for a patient is becoming more complex. Treatment outcomes for patients with high-risk disease remain poor and choice of maintenance in this population also remains unclear. This review article evaluates up-to-date literature regarding established maintenance approaches. It further analyses ongoing studies exploring maintenance regimens using combination and novel agents, approaches to maintenance in patients with cytogenetic high-risk disease and minimal residual disease response-adapted strategies that reflect the current evolving treatment paradigm.

Endoscopic stenting for malignant gastric outlet obstruction: focusing on comparison of endoscopic stenting and surgical gastrojejunostomy.

Malignant gastric outlet obstruction (GOO) is a condition characterized by blockage or narrowing where the stomach empties its contents into the small intestine due to primary malignant tumors or metastatic diseases. This condition leads to various symptoms such as nausea, vomiting, abdominal pain, and weight loss. To manage malignant GOO, different treatment options have been employed, including surgical gastrojejunostomy (SGJ), gastroduodenal stenting (GDS) using self-expandable metallic stent (SEMS), and endoscopic ultrasound-guided gastrojejunostomy (EUS-GJ). This review focuses on comparing the clinical outcomes of endoscopic stenting (GDS and EUS-GJ) with SGJ for malignant GOO. Studies have shown that GDS with SEMS provides comparable clinical outcomes and safety for the palliation of obstructive symptoms. The choice between covered and uncovered SEMS remains controversial, as different studies have reported varying results. EUS-GJ, performed via endoscopic ultrasound guidance, has shown promising efficacy and safety in managing malignant GOO, but further studies are needed to establish it as the primary treatment option. Comparative analyses suggest that GDS has higher recurrence and reintervention rates compared to EUS-GJ and SGJ, with similar overall procedural complications. However, bleeding rates were lower with GDS than with SGJ. Randomized controlled trials are required to determine the optimal treatment approach for malignant GOO.

Surgical Outcomes in Uveitic Glaucoma: Long-Term Evaluation of Trabeculectomy, Non-Penetrating Deep Sclerectomy, Ex-PRESS Shunt and Ahmed Glaucoma Valve. A 3-Year Follow-Up Study

ABSTRACT Purpose Uveitis can lead to secondary glaucoma, a condition with challenging management that can carry irreversible visual loss. Filtering surgery has demonstrated a higher failure rate, increased incidence of postoperative complications and reinterventions in uveitic patients. There is no consensus on the optimal surgical approach for uveitic glaucoma (UG) due to limited data comparing various intraocular pressure (IOP)-lowering surgeries. This retrospective cohort aims to assess the clinical outcomes of trabeculectomy (TBT), non-penetrating deep sclerectomy (NPDS), Ex-PRESS shunt and Ahmed glaucoma valve, providing additional insights into the long-term IOP control and safety of filtering surgeries in UG. Methods The filtering surgery was performed on 32 eyes of 27 UG patients. Complete success was defined as IOP ≤ 18 mmHg or a 30% reduction. Qualified success allowed topical hypotensive treatment. Results Complete success was 40.63% (13/32) at 12 months and 36.67% (11/30) at 36 months. Qualified success was 84.38% (27/32) at 12 months and 63.33% (19/30) at 36 months. In the survival analysis, both NPDS and Ex-PRESS demonstrated decreased failure rates compared to TBT (NPDS vs TBT: HR = 0.20, p = 0.049; Ex-PRESS vs TBT: HR = 0.28, p = 0.13). One or more reinterventions were required in 34.38% (11/32) of the eyes. NPDS had the lowest incidence of hypotony. Secondary cataract was a common complication in all groups. Conclusion Various filtering surgeries are safe and effective procedures for lowering IOP and reducing the requirement of topical antihypertensives in UG at 36 months. However, one-third of the patients will require another IOP-lowering procedure.

Mechanical characterization and water stability of loess improved by bio-based materials: An eco-friendly approach

Loess exhibits poor engineering properties, such as low strength and poor water stability. Conventional materials used for improving loess, such as cement and lime, result in environmental pollution issues throughout their production and application processes. To assess the efficacy of bio-based materials, including calcium alginate (CA), xanthan gum (XA), cotton fibers (CO) and flax fibers (FA) in the treatment of loess, the improved soil's strength, disintegration, and water resistance were examined. Subsequently, an optimal amendment approach was determined, and dry-wet cycle tests and microscopic observation were performed. The results show that 1.0 % calcium alginate can effectively enhance the strength of loess, significantly improving its resistance to disintegration with almost no observable disintegration; permeability is significantly reduced, and water repellency is enhanced. 2.0 % xanthan can improve the strength and disintegration resistance of loess, but the improvement in strength is lower than that of calcium alginate. Additionally, the improved soil with XA experiences a flocculent disintegration in static water, which cannot maintain the soil structure. Cotton fibers and flax fibers can enhance both compressive and tensile strength of the soil. The content of 0.45 % flax fibers is considered the optimal choice as it has no effect on water stability. Combining the above results, the combination of 1.0 % CA and 0.45 % FA has been selected to improve the loess, which effectively improves the comprehensive mechanical properties and water stability of the composite improved soil. The decrease in strength and mass loss rate are significantly reduced after dry-wet cycle tests. Microscopic tests show that calcium alginate connects soil particles by Ca2+ ionic bridges, which allows the cementing materials to fill the loess pores and exert the role of agglomeration and coagulation to enhance the integrity of the loess. This study shows that the bio-based material with calcium alginate as the main body can effectively improve the mechanical strength and water stability of the loess.

Enhancing algal production strategies: strain selection, AI-informed cultivation, and mutagenesis

Microalgae are emerging as a sustainable source of bioproducts, including food, animal feed, nutraceuticals, and biofuels. This review emphasizes the need to carefully select suitable species and highlights the importance of strain optimization to enhance the feasibility of developing algae as a sustainable resource for food and biomaterial production. It discusses microalgal bioprospecting methods, different types of cultivation systems, microalgal biomass yields, and cultivation using wastewater. The paper highlights advances in artificial intelligence that can optimize algal productivity and overcome the limitations faced in current microalgal industries. Additionally, the potential of UV mutagenesis combined with high-throughput screening is examined as a strategy for generating improved strains without introducing foreign genetic material. The necessity of a multifaceted optimization approach for enhanced productivity is acknowledged. This review provides an overview of recent developments crucial for the commercial success of microalgal production.

Data-driven distributionally robust optimization approach for the coordinated dispatching of the power system considering the correlation of wind power

With the increasing penetration of large-scale wind power into the power grid, it is crucial to develop a precise model to accurately depict the stochasticity and correlation among wind farm outputs, which is highly important for ensuring the safe and efficient utilization of wind energy in grid dispatching. In this article, a data-driven distributionally robust optimization (DDRO) dispatching approach that accounts for spatial correlations among outputs from multiple wind farms is proposed. The proposed approach is applied to a source-network-load-storage grid system to ascertain unit start-stop schedules and resource allocation effectively. First, a truncated spatial correlation model is proposed, enabling a comprehensive representation of spatial correlations and output constraints between distinct wind farms. Second, the ISODATA clustering algorithm is employed to generate typical scenarios, reduce model complexity, and expedite the computation process. Third, a unit commitment model considering the demand response is constructed and solved using the DDRO approach. Finally, the proposed model is applied to the IEEE 30-bus system to test its robustness and cost-effectiveness compared to the traditional robust optimization model. Additionally, it is applied to the IEEE 118-bus system to demonstrate its scalability and stability.

Multilevel optimization for policy design with agent-based epidemic models

Epidemiological modeling has a long history and is often used to forecast the course of infectious diseases or pandemics. These models come in different complexities, ranging from systems of simple ordinary differential equations (ODEs) to complex agent-based models (ABMs). The former allow a fast and straightforward optimization, but are limited in accuracy, detail, and parameterization, while the latter can resolve spreading processes in detail, but are extremely expensive to optimize. Epidemiological modeling can also be used to propose and design non-pharmaceutical interventions such as lockdowns. In general, their optimal design often leads to nonlinear optimization problems. We consider policy optimization in a prototypical situation modeled as both ODE and ABM, review numerical optimization approaches, and propose a heterogeneous multilevel approach based on combining a fine-resolution ABM and a coarse ODE model. Numerical experiments, in particular with respect to convergence speed, are given for illustrative examples.

Risk-based dynamic pricing by metaheuristic optimization approach for electric vehicle charging infrastructure powered by grid integrated microgrid system

This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature.

Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem

Improving Operating Room, Surgery, and Anesthesiologist Scheduling Using Stochastic and Robust Optimization Efficient planning and scheduling of operating room (OR) activities is crucial for managing costs and delivering high-quality surgical care. However, this task is extremely complex for several reasons. First, it requires coordinating multiple resources, such as ORs and anesthesiologists. Second, in addition to limited OR capacity and time, there is a significant shortage of anesthesiologists required to perform surgeries. Third, surgery durations are uncertain and difficult to predict. Ignoring such uncertainty may lead to substantial overtime, idling, and surgery delays, among other schedule deficiencies. Thus, hospital managers could benefit greatly from advanced methodologies to improve OR utilization, surgical care, and quality as well as to minimize OR operational costs. In “Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem,” M. Y. Tsang, K. S. Shehadeh, F. E. Curtis, B.R. Hochman, and T. E. Brentjens propose computationally tractable stochastic programming and distributionally robust optimization methodologies for an integrated allocation, assignment, sequencing, and scheduling problem under uncertainty involving multiple ORs, anesthesiologists, and surgery types. Using real-world surgery data and a case study from a health system in New York, they conduct extensive experiments demonstrating the computational efficiency of the proposed methodologies, allowing for their implementation in practice. Moreover, they show the negative consequences of adopting the existing non-integrated approaches and provide valuable practical insights.

Optimizing the Fixed Number Detector Placement for the Nuclear Reactor Core Using Reinforcement Learning

Monitoring three-dimensional flux distribution in a nuclear reactor core is essential for improving safety and economics, which requires strategically placed in-core detectors. However, the deployment of these sensors is often constrained by physical, industrial, and economic limitations. This study treats optimizing the location of in-core detectors as a Markov decision process and develops a reinforcement learning (RL)–based framework to provide a solution for detector placement given a fixed number of detectors and available detector positions. The RL-based framework contains an environment consisting of a Proper Orthogonal Decomposition–based power reconstruction function paired with a novel reward function based on the power reconstruction error and a well-educated agent that updates the detector placement. Four RL algorithms including Proximal Policy Optimization, Deep Q-Network, Advantage Actor-Critic, and Monte Carlo Tree Search are investigated to optimize the detector placement and are analyzed. Genetic Algorithm (GA), a traditional optimization approach, is applied for comparison. The findings reveal that RL outperforms GA in terms of the quality of optimal solutions, demonstrating an inclination toward locating a global solution. Moreover, the flexible nature of RL enables the integration of developed novel reward functions from a specific reactor core into other reactors, considering the particular engineering requirements within the RL-based framework, thereby enhancing the optimization of in-core detector configurations.

Comparison between transradial and transfemoral mechanical thrombectomy for ICA and M1 occlusions: insights from the Stroke Thrombectomy and Aneurysm Registry (STAR)

BackgroundThe role for the transradial approach for mechanical thrombectomy is controversial. We sought to compare transradial and transfemoral mechanical thrombectomy in a large multicenter database of acute ischemic stroke.MethodsThe prospectively...

Optimal energy storage system design for addressing uncertainty issues in integration of supply and demand-side management approaches

The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes.

Does a simultaneous ventral/dorsal approach provide better reduction quality in treating acetabular fracture involving both columns with displaced posterior wall?

Various surgical techniques have been proposed to manage acetabular fractures involving both columns with posterior wall displacement. However, the optimal surgical approach to achieve satisfactory reduction quality remains controversial. This retrospective study evaluated 34 patients with fractures who were treated at a single medical institution. The patients were divided into two groups according to the ventral/dorsal surgical approach employed: simultaneous (SI) and sequential (SE). Perioperative parameters, as well as radiological and functional outcomes, were analyzed and compared between the two groups. The SI and SE groups comprised 9 and 23 out of the 34 patients, respectively. The SI group exhibited a significantly shorter surgical time and lower estimated blood loss than the SE group (p = 0.04 and 0.03, respectively). The quality of reductions of the anterior and posterior columns was similar between the two groups; however, superior reduction in the fracture gap of the posterior wall was observed in the SI group, as revealed by axial and coronal computed tomography scans. A simultaneous ventral and dorsal approach through the pararectus and the modified Gibson approach confer clinical advantages in reducing the fracture gap, surgical time, and intraoperative blood loss when managing acetabular fractures involving both columns and a displaced posterior wall. Therefore, these surgical approaches may be considered to be optimal for achieving satisfactory reduction quality in such fractures.

Optimal structure identification of bilinear-Laguerre model from input/output observations

This paper proposes a new approach to identify an optimal structure with respect to the truncating orders of the new nonlinear bilinear-Laguerre model. This latter is used to describe a nonlinear system with a parametric complexity reduction resulting from the expansion of the bilinear model on independent and orthonormal Laguerre bases. The proposed structural identification approach is investigated to guarantee from input/output observations a more accurate model approximation of the system with significant parameter reduction. This is achieved by identifying the optimal truncating orders characterizing the model’s structure. This approach is based only on the filtered input/output of the system by defining a specific matrix, which becomes singular when the truncating orders are close to their optimal values. According to the matrix singularity of the specific matrix by varying the truncating orders, we propose a structural identification algorithm where an estimation indicator is considered to achieve the optimal structure identification. This technique allows us to calculate the optimal truncating orders where the noise influence is studied. It is tested in numerical examples based on the bilinear-Laguerre model with a variable structure by changing the value of the truncating orders. As well, experimental validation is retained on a 2-degree-of-freedom (DoF) helicopter system. Simulation and experimental results show the effectiveness of the proposed optimal structure identification approach.

Structural damage identification through unscented Kalman inversion with l 1-norm regularization

Advances in modal analysis have motivated finite element model updating for damage identification based on vibration monitoring. However, the model updating problem is usually ill-posed owing to limited and noisy measurement data, and the predominant sensitivity-based optimization approaches may suffer from large linearization errors. To address these challenges, this article proposes using the unscented Kalman inversion with l 1 -norm regularization (UKI- l 1 ) to solve the damage identification problem in a derivative-free manner. UKI- l 1 implements an explicit transformation converting the l 1 -norm regularized least-squares problem into the l 2 -norm regularized problem, which can be readily solved by the UKI with measurement augmentation. The effectiveness of the proposed approach is evaluated through identifying damage of a continuous beam structure and a reinforced concrete frame structure. Numerical investigation confirms the robustness of UKI- l 1 to measurement noise, and experimental study demonstrates that the damage identification results obtained by UKI- l 1 match well with expectations.

Sustainable supply chain planning with the flexible provisions of inter-period emission credits borrowing and banking under a multi-period carbon trading scheme

Although the carbon emission trading regulation proves its advantage of reducing abatement costs, the timing of operating activities makes overage or shortage of allocated emission allowances quickly happen in single-year compliance, thus increasing abatement costs. The provision of inter-period emission credits banking and borrowing in a multi-period compliance phase can give emitters flexibility in emission compliance, which practices world-widely. In this study, we investigate the impact of carbon trading with inter-temporal emission credits borrowing and banking on a production–inventory–distribution system’s supply chain network decision-making and its compliance strategies by proposing a novel two-stage stochastic programming. A scenario-based robust optimization approach is presented with a post-optimization stage to obtain robustness-profit-environment balanced decision-making considering uncertain demand and stochastic carbon-trading price. The results suggests emitters should adopt specific greener techniques to reduce related emissions with priority under carbon-trading policies. The results also show the provision of carbon credits borrowing and banking has a positive influence on enhancing the efficiency in supply chain economic and ecological performance. Besides, given the flexible provisions, the manager can reallocate emission credits according to its business’s operation, and transfer more emission credits to periods of higher carbon-trading price. This findings also suggest banking in early periods instead of carelessly borrowing from future, for uncertainty in future business might make it harder or impossible to comply with emission regulation at last. To regulators, it should prevent borrowing explicitly or allow it only to a limited extent.

A preview SMC driver modeling and simulation for the B-train doubles

B-train doubles have existed a greater risk of causing accidents. The research on the driver model for combination vehicles is crucial to the active safety control of B-train doubles. The direction control of the driver model forms an invaluable part of the closed-loop simulation of the human-vehicle-road system. Meanwhile, studies of driver models for long combination vehicles (LCVs) are limited. Driver modeling for the articulated heavy vehicle (AHV), a preview driver model for B-train doubles, has been proposed, on the basis of Sliding Mode Control (SMC), considering the low-speed path-tracking and high-speed stability, with the constant speed approach rate and optimal approach rate of the SMC, respectively. The simulation proved that, the proposed driver model can significantly eliminate the chattering phenomena of the front wheel angle, enhance the lateral stability of the B-train doubles, and decrease in peak magnitude of lateral acceleration and yaw angular velocity, and achieve better path following ability.