Optimal Size Research Articles

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ДИЭЛЕКТРИЧЕСКИХ СВОЙСТВ ПЕРЕРАБОТАННЫХ ЗЕРНОВЫХ

The purpose of research is to study the dielectric properties of grains processed by mechanical activation using the example of wheat, barley and oats, which are widely used in the agro-industrial complex and the food industry. Objective: to identify the relationship between the dielectric parameters of processed grain and its structural parameters (fraction size) in a wide range of variations in the external electric field from 50 Hz to 1 KHz, which may be useful in optimizing grain processing technologies and choosing optimal storage conditions. A series of experiments was carried out for samples of wheat, oats and barley, varying the degree of fractions from 50 to 1000 μm in a wide frequency range. An algorithm has been developed for calculating dielectric constant and dielectric loss tangent based on measurements of electrical capacitance and conductivity using a certified E7-20 voltage immittance meter and a measuring cell (flat capacitor). An analysis of variations in electrical capacitance, the real part of dielectric constant, electrical conductivity and dielectric loss tangent on the particle size of grain crops was carried out. It has been established that there is a correlation between the dielectric properties of grain processed by mechanical activation and the size of the fractions, which is most significant for small-sized samples with a particle size of no more than 250 microns. A smoothing of variations in dielectric constant and losses was noted as the electric field frequency increased above 100 Hz. The existence of a relationship between the energy properties of the studied fine heterogeneous medium and the degree of grinding in the frequency range under study has been experimentally confirmed. An optimal size for grain processing has been proposed, which will, along with saving energy resources, allow optimizing energy value, improving the quality of food products and the efficiency of agricultural production in general.

Manipulating Aggregation Kinetics toward Efficient All-Printed Organic Solar Cells.

The power conversion efficiencies (PCEs) of all-printed organic solar cells (OSCs) remain inferior to those of spin-coated devices, primarily due to morphological variations within the bulk heterojunction processed via diverse coating/printing techniques. Herein, cyclohexyl is introduced as outer side chains to formulate a non-fullerene acceptor, BTP-Cy, aimed at modulating the molecular aggregation in solution and subsequent film formation kinetics during printing. Investigations demonstrate that BTP-Cy molecule with cyclohexyl side chains exhibits enhanced intermolecular π-π stacking, optimal solution aggregation size, and favorable phase separation. Consequently, PB3:FTCC-Br:BTP-Cy-based OSCs achieve remarkable PCEs of 20.2% and 19.5% via spin-coating and blade-coating, respectively. Furthermore, a 23.6 cm2 module exhibits a remarkable efficiency of 16.7%. This study offers a fresh perspective on tailoring the film formation kinetics of photoactive materials during printing through molecular design, paving a novel path to enhance the efficiency of all-printed OSCs.

Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria

Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables.

Explainable correlation-based anomaly detection for Industrial Control Systems

Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). However, the complicated structure of ICS creates complex temporal correlations among devices with many parameters. Current methods often ignore these correlations and poorly select parameters, missing valuable insights. Additionally, they lack interpretability, operating efficiently with limited resources, and root cause identification. This study proposes an explainable correlation-based anomaly detection method for ICS. The optimal window size of the data is determined using Long Short-Term Memory Networks—Autoencoder (LSTM-AE) and the correlation parameter set is extracted using the Pearson correlation. A Latent Correlation Matrix (LCM) is created from the correlation parameter set and a Latent Correlation Vector (LCV) is derived from LCM. Based on the LCV, the method utilizes a Multivariate Gaussian Distribution (MGD) to identify anomalies. This is achieved through an anomaly detection module that incorporates a threshold mechanism, utilizing alpha and epsilon values. The proposed method utilizes a novel set of input features extracted using the Shapley Additive explanation (SHAP) framework to train and evaluate the MGD model. The method is evaluated on the Secure Water Treatment (SWaT), Hardware-in-the-loop-based augmented ICS security (HIL-HAI), and Internet of Things Modbus dataset using precision, recall, and F-1 score metrics. Additionally, SHAP is used to gain insights into the anomalies and identify their root causes. Comparative experiments demonstrate the method's effectiveness, achieving a better 0.96% precision and 0.84% F1-score. This enhanced performance aids ICS engineers and decision-makers in identifying the root causes of anomalies. Our code is publicly available at a GitHub repository: https://github.com/Ermiyas21/Explainable-correlation-AD.

Siting and Sizing Method of GFM Converters Based on Genetic Algorithm

The rising integration of renewable energy sources has resulted in a diminished capacity for voltage support within the system, which is characterized by low inertia and a reduced short circuit ratio (SCR). In order to improve grid strength and enhance the capacity for renewable energy integration, an initial analysis was conducted on the grid support capabilities of grid-forming (GFM) stations, followed by an investigation into how grid strength influences the dominant operational modes of GFM converters. Subsequently, leveraging the definition of the multi-infeed short circuit ratio, a calculation method for the SCR, applicable to new energy base stations featuring GFM substations, is developed. Additionally, a strategic approach to optimal location selection and sizing of these substations aimed at enhancing the SCR within new energy grids is proposed, with the model being solved through genetic algorithms. Finally, the effectiveness of the proposed method is verified based on the IEEE39-node system and a real new energy station. The results show that the system strength is greatly improved after the optimized configuration of the GFM equipment, and the maximum tolerable space of 90% new energy stations reaches 95% of the theoretical maximum tolerable space of each new energy station.

Gravity-based microfiltration reveals unexpected prevalence of circulating tumor cell clusters in ovarian and colorectal cancer

BackgroundCirculating tumor cells (CTCs) are rare (a few cells per milliliter of blood) and mostly isolated as single-cell CTCs (scCTCs). CTC clusters (cCTCs), even rarer, are of growing interest, notably because of their higher metastatic potential, but very difficult to isolate.MethodWe introduce gravity-based microfiltration (GµF) for facile isolation of cCTCs using in-house fabricated microfilters and 3D printed cartridges. Optimal flow rate and pore size for cCTC isolation are determined by GµF of cultured ovarian single cells and cell clusters spiked in healthy blood. We perform GµF of blood from orthotopic ovarian cancer mouse models and characterize the morphological features of scCTCs and cCTCs, and the expression of molecular markers for aggressiveness. Finally, we analyze blood from 17 epithelial ovarian cancer patients with either localized or metastatic disease, and from 13 colorectal cancer liver metastasis patients.ResultsHere, we show that GµF optimized for cell cluster isolation captures cCTCs from blood while minimizing unwanted cluster disaggregation, with ~85% capture efficiency. We detect cCTCs in every patient, with between 2–100+ cells. We find cCTCs represent between 5–30% of all CTC capture events, and 10-80% of the CTCs are clustered; remarkably, in 10 patients, most CTCs are circulating not as scCTCs, but as cCTCs.ConclusionsGµF uncovers the unexpected prevalence and frequency of cCTCs including sometimes very large ones in epithelial ovarian cancer patients, and motivates additional studies to uncover their properties and role in disease progression.

EVCS Demand Management using Multi-Objective Power Flow Analysis with Renewable Distributed Generators

Abstract The optimal placement and sizing of multiple distributed generator units in strategic locations are essential for effective energy management of Electric vehicle charging station (EVCS) in modern power systems. This study aims to minimize power losses, optimize Renewable based Distributed Generator (RDG) sizing, and reduce fuel costs of conventional generators while also considering the impact on voltage stability by using multi objective Newton-Raphson (NR) power flow method. Two meta-heuristic multi-objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Differential Evolution (MODE) techniques have been merged with NR power flow analysis. In this paper two scenarios are analyzed: the base case under normal conditions and with EVCS where the load is increased by 1.1 times the base load. The results demonstrate that the MOGWO approach outperforms the MODE method, yielding more favorable outcomes in both scenarios. Notably, the MOGWO method results in smaller RDG sizes compared to MODE in both normal and EVCS loading conditions. Transmission losses are reduced by 68.12% under normal conditions and by 72.7% under EVCS loading conditions with the MOGWO approach. The effectiveness of the proposed method has been evaluated using MATLAB software on the standard IEEE 57-bus system. Additionally, the base case results have been compared with other algorithms, including SCA, DA, NSGA-II, and MOCS, to validate the performance of the proposed MOGWO approach.

An Innovative Approach on Recycle Foam Concrete as a Sustainable Alternative with the addition of Nano Titanium Dioxide TiO2 on the Properties of Foam Concrete

Sustainability and construction waste recycling have become crucial topics today in response to the growing environmental challenges and the increasing accumulation of waste. Therefore, it is essential to explore innovative solutions that improve the sustainability of concrete mixes. An effective approach is the use of Lightweight Foamed Concrete (LFC), a revolutionary new material that is considered a viable solution for the reduction of the weight of conventional concrete. This research focuses on the study of the effect of replacing 50% of virgin sand by volume with Recycled Foam Concrete (RFC) waste crushed at four gradation levels with aggregate sizes between 12.5-9.5 mm, 9.5-4.75 mm, 4.75-2.36 mm, and 2.36-1.18 mm, and the effect of adding 0.5% Nano titanium dioxide TiO2 by weight of cement. The water-to-cement and cement-to-aggregate ratio were maintained at 0.45 and 1:1.3, respectively. Nanoparticles are incorporated into Foam Concrete (FC) to enhance its strength, due to their beneficial properties, such as their small particle size and high reactivity. The results conclude on the optimal sizes of RFC with the addition of Nano TiO2 for use in FC mixes that enhance compressive strength and increase carbonation compared to traditional FC mixes.

Optimal sizing and cost-benefit assessment of stand-alone microgrids with different energy storage considering dynamic avoided GHG emissions

Optimal sizing and cost-benefit assessment of stand-alone microgrids with different energy storage considering dynamic avoided GHG emissions

Optimal sizing and operation of microgrid considering renewable energy uncertainty based on scenario generation

Optimal sizing and operation of microgrid considering renewable energy uncertainty based on scenario generation

In Vitro Potentiation of Doxorubicin Cytotoxicity Utilizing Clarithromycin Loaded-PEGylated Liposomes.

Doxorubicin (DOX) is a potent chemotherapeutic agent for breast cancer, but its effectiveness is often diminished by resistance mechanisms, particularly through p-glycoprotein (P-gp) mediated drug efflux. Clarithromycin (CAM), a macrolide antibiotic, inhibits multiple metabolic pathways including CYP3A and P-gp, potentially countering DOX resistance. This study aimed to evaluate the potentiation of DOX and its effectiveness against the MCF-7 breast cancer cell line by encapsulating both DOX and CAM in PEGylated liposomes. PEGylated liposomes containing DOX and CAM were prepared using the thin film hydration method. The physicochemical properties of the liposomes, including average particle size, polydispersity index (PDI), and zeta potential, were characterized. Encapsulation efficiencies for CAM and DOX were assessed, and stability of the liposomes was evaluated over 9 days at room temperature. Cell viability was measured using an IC50 assay, and P-gp expression levels were determined by ELISA. The CAM/DOX-PEGylated liposomes exhibited optimal average particle size (238 ± 26.7 nm), PDI (0.29 ± 0.107), and zeta potential (-20.9 ± 2.17 mV). These liposomes maintained good stability regarding size and charge over 9 days. Encapsulation efficiencies were 81.05% for CAM and 78.13% for DOX. The IC50 value for CAM/DOX-PEGylated liposomes was 0.13 µM, representing a significant reduction compared to the physical mixture of CAM and DOX (0.25 µM) and free DOX (0.21 µM) against MCF-7 cells. ELISA analysis showed a reduction in P-gp expression of approximately 5% with CAM/DOX-PEGylated liposomes compared to 1.61% with free DOX. The results indicate that CAM encapsulated in PEGylated liposomes enhances the effectiveness of DOX against breast cancer cells, likely through the inhibition of p-glycoprotein. This approach may offer a promising strategy to overcome DOX resistance and improve chemotherapy outcomes.

Integration of GWAS models and GS reveals the genetic architecture of ear shank in maize.

Maize is one of the most important crops for human food, animal feed, and industrial raw materials. Ear shank length (ESL) and ear shank node number (ESNN) are crucial selection criteria in maize breeding, impacting grain yield and dehydration rate during mechanical harvesting. To unravel the genetic basis of ESL and ESNN in maize, an association panel consisting of 379 multi-parent doubled-haploid (DH) lines was developed for genome-wide association studies (GWAS) and genomic selection (GS). The heritabilities of ESL and ESNN were 0.68 and 0.55, respectively, which were controlled by genetic factors and genotype-environment interaction factors. Using five different models for GWAS, 11 significant single nucleotide polymorphisms (SNPs) located on chromosomes 1, 2, and 4 were identified for ESL, with the phenotypic variation explained (PVE) value of each single SNP ranging from 4.91% to 21.35%, and 11 significant SNPs located on chromosomes 1, 2, 4, and 5 were identified for ESNN, with the PVE value of each SNP ranging from 1.22% to 18.42%. Genetic regions in bins 1.06, 2.06, and 2.08 were significantly enriched in SNPs associated with ear shank-related traits. The GS prediction accuracy using all markers by the five-fold cross-validation method for ESL and ESNN was 0.39 and 0.37, respectively, which was significantly improved by using only 500-1000 significant SNPs with the lowest P-values. The optimal training population size (TPS) and marker density (MD) for ear shank-related traits were 50%-60% and 3000, respectively. Our results provide new insights into the GS of ear shank-related traits.

Multi-Objective Feature Selection Algorithm using Beluga Whale Optimization

The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06% across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54% higher than all other methods.

Consignment stock partnership in multi-vendor multi-buyers supply chains

ABSTRACT In this paper, we present an integrated optimization model for simultaneously addressing the problem of optimizing delivery quantities and determining optimal production lot sizes in a consignment stock partnership between vendors and buyers. The objective is to minimize the total cost of ordering, holding, setup, and transportation in a two-echelon supply chain. We propose three coordination policies to solve this problem. The first policy involves each vendor producing and delivering a batch to all customers in equal and proportional quantities to their demand. The second policy involves vendors only delivering the product upon receiving an order. The third policy involves each vendor making deliveries to all customers, but not necessarily at the same time. Additionally, a fourth model integrating the previous three policies is proposed. Numerical examples demonstrate the benefits of this integrated model, while sensitivity analyses highlight the impact of key parameters on the total cost.

Optimal Bone Preparation for Triple-Tapered Stems: Does the Sagittal Taper Affect Optimal Femoral Sizing?

Conventional single-tapered, total hip arthroplasty stems achieve fixation namely through coronal, metaphyseal fit. Triple taper stems have a sagittal taper to optimize fixation in the antero-posterior (AP) plane as well; however, limited guidance exists on appropriate bone preparation. Often, similar preparation techniques are used despite geometric differences which may lead to underpreparation. We've defined a novel technique in which a small portion of posterior femoral neck and cancellous bone is removed to permit preparation collinear to the diaphyseal sagittal femoral axis. We hypothesize this will optimize stem fit and stability compared to conventional techniques. This is a retrospective review of 38 cementless primary total hip arthroplasty cases performed by a single surgeon. In each case, broach preparation was initially performed through the center of the femoral neck as although it was a single-tapered stem. Once tactile sensation of adequate fit was achieved, fluoroscopic images were taken to document AP and mediolateral fit, and stem size was recorded. Then that broach was removed, and a standardized one-third of the posterior femoral neck and posterior cancellous bone was removed, permitting broaches to prepare the femur collinear to the femoral diaphyseal sagittal axis- triple-tapered preparation (TTP). Outcomes included change in stem size from initial broach trial to final stem selection and radiographic stem fill on AP and lateral views. Median single-tapered preparation broach size was 8 (range, 5-12) and final stem size after TTP was 11 (range, 6-13). The TTP overall mean percent metaphyseal fill was 74 ± 6% in the AP view and 71 ± 5% in the lateral view, both significantly higher than single-tapered preparation which was 67 ± 7% and 65 ± 7%, respectively (P < .001). No fractures or loosening occurred in this series. Preparation of triple-tapered stems collinear to the diaphyseal sagittal femoral axis improves stem size, fit, and fill.

Drug-loaded liposomes for macrophage targeting in Mycobacterium tuberculosis: development, characterization and macrophage infection study.

This study investigates drug-loaded liposomes targeting macrophages as a promising strategy to enhance Tuberculosis (TB) treatment. The focus is on optimizing liposomal formulations for encapsulating OX-23, a previously identified anti-mycobacterial agent with a minimum inhibitory concentration (MIC) of 1.56µg/ml, and assessing their efficacy in macrophage infection models. Liposomal formulations were characterized for particle size, polydispersity index (PDI), and zeta potential using dynamic light scattering (DLS), with morphology analyzed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Macrophage infection assays, including those with the THP-1 macrophage cell line, were performed to evaluate the targeting efficiency and therapeutic potential of the formulations. Results showed that OX-23 could be successfully encapsulated in liposomes with various charges, achieving high encapsulation efficiency, optimal particle size, and acceptable PDI values. In-vitro studies with the THP-1 cell line demonstrated sustained release of the drug from the liposomes, with morphological analysis confirming that the liposomes were spherical and non-aggregated. The formulations exhibited significant penetration into infected macrophages and effectively inhibited the growth of intracellular Mycobacterium tuberculosis at the tested concentrations. These findings support the potential of liposomal OX-23 in targeting both extracellular and intracellular M. tuberculosis, offering a promising approach to TB treatment.

Optimization of formulation and device design for carrier-based nasal powder of influenza vaccine to maximize the delivery capability to the target site in the nasal cavity

Previously, a nasal powder formulation of the influenza vaccine was proposed to ensure its stability over fifteen months at room temperature and to have sufficient moisture-resistant characteristics to enable handling without special humidity control. In this study, a carrier-based concept: a physical mixture of the antigen powder and the carrier powder, was adopted for this nasal powder formulation. The physical properties of both components were optimized to maximize the delivery capability to the target site located behind the nasal cavity using a human nasal cast model. Preliminary evaluation with a prototype device (Device A) revealed that the physical properties of the antigen powder influenced the delivery efficiency mainly through their effect on the discharge rate from the device. Since it is obvious that the discharge rate from the device must be secured as a prerequisite for increasing the target site delivery, Device B with an improved discharge rate was developed, and the optimal powder properties were re-evaluated. The delivery rate of antigen powder to the target site was broken down into two elements: the discharge rate from the device and the delivery efficiency of emitted powder to the target site, and then the effect of the particle size of antigen powder and the carrier powder on each element was analyzed separately. As a result, an optimal particle size region was identified for each element as a design space on a two-dimensional map described by the antigen particle size and the carrier particle size. Besides this analysis, the fine particle fraction (aerodynamic particle size less than 5 μm) was evaluated in a separate experiment considering the risk of invasion into the lung and a condition of particle size to minimize the fine particle fraction was identified. By integrating the results of the above analysis, an optimal space was determined on the two-dimensional map to maximize the delivery efficiency to the target site while minimizing unintended exposure to the lungs.

Robust capacity sizing and operation scheduling optimisation for resilient precinct-scale energy–water system integration

Robust capacity sizing and operation scheduling optimisation for resilient precinct-scale energy–water system integration

Multi-objective optimization and posteriori multi-criteria decision making on an integrative solid oxide fuel cell cooling, heating and power system with semi-empirical model-driven co-simulation

An integrative solid oxide fuel cell combined cooling, heating and power system in green buildings with hydrogen energy of byproduct water enables carbon neutrality transformation. However, underlying mechanisms on capacity sizing of combined cooling, heating and power system devices and its impacts on system techno-economy have not been figured out especially considering dynamic degradation and efficiency of associated devices. In this study, a multi-software optimization platform is established by MATLAB-TRNSYS co-simulation for sizing parametrical analysis, with well balance of modelling complexity and computational efficiency. A self-sufficient combined cooling, heating and power system is modelled integrating with a semi-empirical surrogate model of solid oxide fuel cell to interact with other balance of plant types efficiently. Total energy efficiency and annual total cost are optimized through parametrical analysis on device size of each component (battery, electrolyzer and solid oxide fuel cell) and analysis of variance for contribution ratio quantification. Results indicate that, the size increase in electrolyzer and solid oxide fuel cell will improve system total energy efficiency by 13.635 % and 2.194 %, but promote annual total cost by 4.042 × 104 $ and 2.389 × 103 $, respectively. Besides, sensitivity analysis indicates that the electrolyzer size prioritizes other design parameters in techno-economic performance. Optimal sizes of battery, electrolyzer and solid oxide fuel cell are in cell number range of 333 – 403, 17 – 20, and 26 – 30, respectively, with corresponding optimal total energy efficiency and annual total cost at 70.861 % – 72.147 % and 6.723 × 104 $ – 7.325 × 104 $, respectively. The research results can provide guidance on hydrogen-based cooling, heating and power system design and operation with techno-economic feasibility for low-carbon district energy transition.

A novel optimization approach for the design of environmentally efficient gridshells with reclaimed steel members

The reuse of structural components from decommissioned structures is gaining traction among researchers and industry professionals. This approach offers significant advantages, including reduced costs and a smaller environmental footprint, by incorporating reclaimed elements from dismantled structures into the design of new ones. Steel elements are particularly well-suited to this purpose because they preserve their mechanical properties over time. Nevertheless, integrating reused members into the structure of a gridshell introduces complexities into the design process, as it adds additional parameters related to the characteristics of the reused members themselves, such as cross-section, length, and material. Therefore, optimizing gridshell structures with reused members necessitates analyzing solutions based on the placement of the reused members within the grid, as well as considering grid configurations that accommodate the characteristics of the reused members.This paper presents a novel approach for optimizing steel gridshells that integrates reclaimed members into the structure. The approach effectively combines a geometry and a size optimization technique through a unique process using genetic algorithms. Applied to a case study derived from the literature and considering different scenarios of reused elements, the approach is also compared to a manual design approach. The results and comparisons demonstrate the proposed approach's capability to provide lighter solutions, leading to lower costs and a reduced environmental impact, the last highlighted by the evaluation of the greenhouse gas emission for each case.