Optimal Point Research Articles

A Novel Arithmetic Optimization Technique for Electric Vehicle Charging

With the increased usage of Electric vehicles in recent days, the optimal usage of battery both in terms of electrical and economic perspective is very important. Though several battery optimization techniques are evolving day by day, the method to achieve an efficient control of all parameters like battery current, power and power loss has not yet been achieved. This paper proposes a novel approach of implementing an arithmetic optimization scheme for FTP 75 drive cycle source. In this connect, the details of the arithmetic operators are discussed and their relations with electrical parameters are framed from the fundamental equations. A Pseudo-code has been developed and implemented for identifying the optimum charging point from different search spaces. The simulation results obtained for different electrical parameters with respect to different arithmetic operators, emphasizes the enhanced operation of electric vehicles at optimum charging points.

Improving the decision-making for sustainable demolition waste management by combining a BIM-based life cycle sustainability assessment framework and hybrid MCDA approach.

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle-thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing multifaceted criteria. This study develops a building information modelling (BIM)-based DWM sustainability assessment approach to facilitate the life cycle assessment (LCA) and decision-making by coupling the enriched Industry Foundation Classes model with hybrid multi-criteria decision-aiding (MCDA) methods using Dynamo visual scripting. To streamline the data-intensive LCA process, this study enriched the BIM properties and accommodated them into the LCA data template to enhance data interoperability, thus achieving seamless data transfer. Moreover, hybrid MCDA methods are integrated into the decision-making workflow for DWM scenario ranking. A pilot study is employed to verify the applicability of the decision-aiding framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the 'status quo' in China's recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the 'growth curve' of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the 'Australian standard' scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, signalling that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined framework and contexts.

Multiobjective Optimisation of Flotation Variables Using Controlled-NSGA-II and Paretosearch

Finding the optimum operating points for the maximisation of flotation recovery and concentrate grade can be a very difficult task, owing to the inverse relationship that exists between these two key performance indicators. For this reason, techniques that can accurately find the trade-off are critical for flotation process optimisation. This work extracted well-assessed Gaussian process predictive functions as objective functions for a comparative multiobjective optimisation study using the paretosearch algorithm (PA) and the controlled elitist non-dominated sorting genetic algorithm (controlled-NSGA-II). The main aim was the concomitant maximisation of the copper recovery and the concentrate grade. Comparison of the two applied techniques revealed that the PA discovered the best set of the pareto-optimal solution for both the recovery (93.4%) and concentrate-grade (17.4 wt.%) maximisation.

Weight loss and electrochemical measurements of aluminum corrosion in diesel fuel and biodiesel prepared via transesterification of waste cooking oil

Recent studies have found potentials in the utilization of biodiesel from renewable energy sources to tackle environmental pollution associated with the release of exhausts from fossil diesel fuel engines. However, it's imperative to investigate the corrosion rate and nature of the engine components fuelled with the biodiesel. This study examines the corrosion rate and nature of aluminum coupon (AC) in fossil diesel fuel (DF) and biodiesel produced from waste cooking oil via transesterification reaction (WCOB) using weight loss and electrochemical measurements. Taguchi Orthogonal Array was used to study the influence of temperature, immersion time, agitation and WCOD/DF volume. Weight loss method revealed highest corrosion rates to be 0.0052 mm y−1 and 0.0381 mm y−1 for AC in DF and WCOB respectively. Corrosion rate increased with increase in immersion time and temperature. A shift in the corrosion potential from -0.192 V to -0.574 V; and -0.178 V to -0.516 V was revealed by potentiodynamic polarization curve for AC in WCOB and DF respectively. Nyquist plots exhibited relatively perfect semicircles of different diameters and similar shape. The developed mathematical models were effective for corrosion rate prediction. Minimum corrosion rates of 1.222 × 10−3 mm y−1 and 1.822 × 10−3 mm y−1 for AC in DF and WCOB are respectively feasible at optimum point. Presence of carbon and oxygen on AC surface; pits formation; and loss of Al ion into WCOB and DF after corrosion were noticed. In conclusion, the corrosion rate of AC in WCOB was higher than the DF counterpart.

Predictive values and correlation of CRIB Score II and perfusion index in assessment of severity of illness in sick preterm neonates: An observational study.

Perfusion index (PI) and CRIB (Clinical risk index of babies) Score II both are minimally invasive tools for prediction of mortality and morbidity in sick neonates. This study aims to know the predictive values of both PI and CRIB Score II in assessment of severity of illness in preterm neonates of 28 to 32 weeks and their correlation. PI and CRIB II Score of 125 sick preterm neonates admitted in NICU were assessed within 12 hours of admission. Severity noted in form of outcome. Correlation between PI and CRIB Score II were observed. Receiver operating characteristic (ROC) curve were plotted for PI and CIRB Score II with outcome (discharged vs expired). Area under curve for perfusion index was 0.776 at 95% confidence interval. Optimum cutoff point based on ROC curve was 1.65 with sensitivity of 84%., specificity of 37.2% with positive predictive value of 70% and negative predictive value of 57%. Area under curve for CRIB Score II was 0.622 (p value < 0.028), optimum cut off point based on ROC curve for CRIB II score was 3.5 with sensitivity 79.10% of specificity of 40% positive predictive value is 43.03% and negative predictive value of 76.9%. Spearman's correlation coefficient between PI and CRIB II score was -0.272 and p value is 0.002. This study concluded that perfusion index (p < 0.001) is better for predicting the severity compared to CRIB II score (p < 0.028). A weak negative correlation exists between PI and CRIB II score (r = -0.272).

Structural Modifications due to Bi-Doping in MAPbBr3 Single Crystals and Their Impact on Electronic Transport and Stability.

Doping strategy in lead halide perovskites is essential to enhance its optoelectrical properties and expand the potential applications. In this work, the mechanisms, for how dopants affect the overall structural, optical, electrical, and chemical properties and stability of lead halide perovskite materials, are investigated. This is done by specifically considering various bismuth (Bi)doping concentrations in MAPbBr3 single crystals grown using the inverse temperature crystallization method. The resultant doped single crystals exhibit a saturation point when Bi concentration exceeds 0.063% which is considered an optimum doping point. The highest thermal stability is also achieved at this doping concentration among the doped single crystals. This study clearly identifies how Bi doping affects the properties of MAPbBr3 and extends to consider stability, which has not been fully considered for MA-based perovskites previously. This will provide a clear understanding of evaluating doped perovskite materials for enhanced material properties, device performance, and stability.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&amp;O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

Multi-objective optimization of power networks integrating electric vehicles and wind energy

In the ever-evolving landscape of power networks, the integration of diverse sources, including electric vehicles (EVs) and renewable energies like wind power, has gained prominence. With the rapid proliferation of plug-in electric vehicles (PEVs), their optimal utilization hinges on reconciling conflicting and adaptable targets, including facilitating vehicle-to-grid (V2 G) connectivity or harmonizing with the broader energy ecosystem. Simultaneously, the inexorable integration of wind resources into power networks underscores the critical need for multi-purpose planning to optimize production and reduce costs. This study tackles this multifaceted challenge, incorporating the presence of EVs and a probabilistic wind resource model. Addressing the complexity of the issue, we devise a multi-purpose method grounded in collective competition, effectively reducing computational complexity and creatively enhancing the model's performance with a Pareto front optimality point. To discern the ideal response, fuzzy theory is employed. The suggested pattern is rigorously tested on two well-established IEEE power networks (30- and 118-bus) in diverse scenarios featuring windmills and PEV producers, with outcomes showcasing the remarkable excellence of our multi-purpose framework in addressing this intricate issue while accommodating uncertainty.

Energy redistribution in railway transition zones by geometric optimisation of a novel transition structure

Railway transition zones are critical regions in railway infrastructure that are subjected to excessive operation-driven degradation due to energy concentration within these zones. This work presents a heuristic approach to optimise the geometry of the transition structure and investigate its influence on the strain energy distribution in the railway transition zones (RTZs), with a specific focus on embankment-bridge transitions equipped with a newly proposed ’Safe Hull-Inspired Energy Limiting Design (SHIELD)’ transition structure. For this purpose, a number of three-dimensional finite element models are used to analyze different geometric profiles of SHIELD in a systematic manner. By altering SHIELD’s geometry across longitudinal, transversal, and vertical directions, the influence of the different geometric profiles on the total strain energy distribution across the trackbed layers (ballast, embankment, and subgrade) is studied in terms of spatial and temporal variations. The results establish the contribution of geometry to energy redistribution in all three directions and present an optimum geometry for the type of transition under study. It is found that among all the profiles, the longitudinal geometric profile of SHIELD has the most significant impact on the strain energy distribution, while the transversal profile primarily influences the ballast layer, and the alteration of vertical profiles enhance the local redistribution of strain energy in the vicinity of the transition interface. The preliminary optimisation (heuristic approach) presented in this work provides the starting point for full-scale optimisation to obtain tailored shapes of transition structures such that there is neither a concentration of energy nor an obstruction in the flow of energy in RTZs.

Aircraft Electrothermal Pulse Deicing

Abstract Ice formation and accumulation on aircraft is a major problem in aviation. Icing is directly responsible for aircraft incidents, limiting the safety of air travel and requiring expensive, and sometimes ineffective deicing strategies. Furthermore, electrification of aircraft platforms leads to difficulties with integration of legacy deicing methods such as pneumatic boots. In this work, we study electrothermal pulse deicing capable of efficient and rapid removal of ice from aircraft wings. The pulse approach enables the efficient melting of a thin (&amp;lt;100 μm) ice layer on the wing surface to limit parasitic heat losses. Only the interface is melted, with the rest of the ice sliding on the melt lubrication layer due to aerodynamic forces. To study pulse deicing, we developed a transient thermal-hydrodynamic numerical model that accounts for multiple phases and materials, specific and latent heating effects, melt layer hydrodynamics, as well as boundary layer effects. To identify optimal deicing strategies, we use our model to study the effects of heater thickness (50 μm &amp;lt; th &amp;lt; 1 mm), substrate electrical insulation thickness (10 μm &amp;lt; ti &amp;lt; 1 mm), pulse duration (0.4 s &amp;lt; Δtpulse &amp;lt; 4.5 s), and pulse energy. Optimum operating points are identified for large (Boeing-747), midsize (Embraer-E175), and small (Cessna-172) aircraft. The scale-dependent thermal-hydraulic model results are used to estimate input conditions required for deicing and integrated into an electrical model considering energy storage, power electronics, integration, and layout, to achieve overall volumetric and gravimetric power density optimization.

The computational complexity of finding stationary points in non-convex optimization

The computational complexity of finding stationary points in non-convex optimization

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Optimal high-pressure correlation for transcritical CO2 cycle in direct expansion solar assisted heat pumps

Heat demand may be met sustainably by a solar-assisted heat pump that CO2 as a refrigerant. This is made possible by the employment of an ecologically benign refrigerant and a renewable energy source that enhances system performance. At the ideal high pressure, the CO2 heat pump running in a transcritical cycle will function at its highest coefficient of performance (COP). In that way, this study aims to present correlations for calculating the optimum high pressure in a CO2 direct expansion solar assisted heat pump (DX-SAHP). In this study a mathematical model for compressor, solar evaporator and gas cooler was developed and validated experimentally. The mean difference between the mathematical model and experimental results are −3.9%. Two new equations are proposed to calculate the optimum high pressure in a CO2 DX-SAHP. The first one considered environment temperature, water outlet temperature and evaporating temperature, which are easy to measure. These facilities the use of correlation to control the heat pump. The second one considered environment temperature, water outlet temperature and solar radiation, which are more suitable for designing CO2 DX-SAHP. A data base with 100 optimum points was used to curve fitting. Another data base with 50 optimum points was used to compare the results obtained from curve fitting and correlations for the optimum high pressure available in the literature. The proposed correlations shown a maximum error lower than 10.2% despite of the correlations available in the literature from which the errors are about 30%.

Investigation on ejector design for CO2 heat pump applications us-ing Dymola

In this paper, the Dymola modelling tool is used to study the influence of ejector design onto the whole heat pump cycle working with carbon dioxide. The cycle is built using the components provided by the TIL Modelica library. It is found that the ejector models in TIL are quite limited, namely by their inability to properly capture the on-design plateau and rapid decrease in performance in off-design operation. Therefore, an in-house state-of-the-art ejector model, originally developed in Python, is implemented as a Dymola object. This model is then calibrated onto CO2 experimental data. The operation of a simple CO2 heat pump system is investigated, with focus on the ejector sizing at fixed geometry. It is found that there exists an ejector size that maximises the COP of the cycle. Furthermore, critical ejector pressure is not reached at this optimum COP point; the ejector is operating well under the on-design regime.

Study on Optimal Production Conditions of Fibrinolytic Kinase Derived from the Nereid Worm, Perinereis aibuhitensis Grub

The fibrinolytic kinase identified in the nereid worm (Perinereis aibuhitensis Grub) displays exceptional kinase activity, stability, and specificity, suggesting its potential as a promising candidate for the advancement of new thrombolytic drugs. In this study, a process was optimized for the production of fibrinolytic kinase using Escherichia coli, and the effects of factors such as inoculum, pH, OD, temperature, inducer concentration, and time on the protein yield were investigated. The optimum points of key parameters were determined by single-factor experiments, and the initial pH, OD, and time were determined to be significant by PB (Plackett–Burman design) with six factors at two levels of experiments. The response surface experiments highlighted the key roles of initial pH and induced OD values, and the convergence of the model and experimental data confirmed the optimal conditions and reasonable fluctuation intervals, which proved the reliability of the model.

Minimisation of losses of consumer quality during storage of cherry fruits under the influence of hydrocooling and protective composition

The need for consumption of fruit products, in particular sweet cherries and cherries, is growing every year in Ukraine. This is driven by national traditions, stakeholder needs, and the population's demand for healthy eating. Cherries are a nutritious fruit that contains such functional ingredients as essential amino acids, vitamins, minerals, dietary fiber, etc. Unfortunately, in terms of long-term storage, cherries quickly lose their quality and have a short harvest and processing period. To increase the shelf life of products, it is advisable to develop and improve methods of storage and processing of cherries. Optimisation of chilling methods for cherries before storage increases the shelf life of fresh fruit and the suitability for fresh consumption. Improving the methods of chilling cherries will help to increase the demand for its fruits by all stakeholders in the European market. The aim of the research was to establish the optimal concentration of acetic and lactic organic acids, which are included in the protective composition for processing cherry fruits, and to determine the level of daily losses of cherry fruits during hydrocooling while maintaining the global optimum point. To establish an effective cooling method, fruit losses caused by diseases of microbiological origin and physiological disorders were determined. During storage, the effect of the concentrations of the protective composition on the indicators of weight loss and the average level of daily fruit loss was studied. A regression model was built and the optimal values of the indicators were determined by stages: a regression model was built on the basis of experimental data and the global optimum point was determined on the basis of the model and a confidence interval for the optimum was built. According to the results of the research, the lowest rate of daily fruit losses at the level of 0,117%-0,121% was recorded during hydrocooling of cherry fruits using organic compositions of lactic and acetic acids in the ratio of their components of 1,25-2,25:1,50-2,25 and 1,75-2,25% with the amount of acetic acid at 1,25%, respectively. The regression analysis showed the optimal value of the average level of daily losses during storage of cherry fruits of the model variety ‘Vstrecha’ – 0,106% at the value of factors x1=2,217 and x2=1,966.

Optimizing a vertical axis wind turbine with helical blades: Application of 3D CFD and Taguchi method

Vertical axis wind turbines (VAWT) with helical blades are being interested in urban areas due to the low dependency on the wind direction and also due to low noise production. In spite of good self-starting and smooth operation as the advantages of this type of turbine, its optimizing has not been reported in literature yet and is the subject of this research. A primary investigation by 3D computational fluid dynamics (CFD) to simulate the air flow about the above type of VAWT in the first step showed the importance of three design variables of tip speed ratio, helical angle, and airfoil chord length. However, in the second step, CFD runs were very time consuming and costly. For considering five values for each design variable, 53=125 cases had to be simulated by 3D CFD. Thus, by the use of Taguchi method and with definition of an orthogonal array named L-25, the number of required 3D CFD simulations reduced from 125 to 25. Then, the optimum values of variables were obtained as 1.33 for the tip speed ratio, 30 degrees for the helical angle, and 0.4 m for the airfoil chord length. At the optimum point, the average power coefficient increased to 0.17542. The presented research is novel for optimizing VAWT with helical blades.

Machine learning optimization of a zero energy building (ZEB) by waste heat recovery in a co-generation system

Waste heat recovery is known as one of the newest methods to increase the energy and exergy efficiencies of thermal systems. The concept has been also developed in co-generation systems where productions of electricity, cooling and heating are considered, simultaneously. This study investigates tri-generation system with a co-generation ammonia-water cycle (AWC) and an organic Rankine cycle (ORC) to supply cooling, heating, and power, simultaneously. As novelty, six different working fluids of Ammonia, R124, R134a, R152a, R245fa and R290 were investigated, as six scenarios for ORC. As result, the R124 was found as the best one and the next investigations were followed just with this fluid. Minimizing total cost and maximizing exergy efficiency at the same time are the main aim of this paper. Design Expert software was used to find out about this optimum point. Four cities in Australia were selected and the effect of annual ambient temperatures of them were studied on the outputs of the system, using TRNSYS software. Also, the optimum values of the effective parameters, including the isentropic efficiencies of pumps, compressor, and turbines, heat source temperature, ammonia concentration, utmost pressures of ORC and AWC, were obtained and reported by means of TOPSIS. As results, the monthly generations in electricity, heating and cooling were estimated. It was revealed that the lower ambient temperature, the higher electricity generation, according to the thermodynamic cycle of the proposed co-generation system. Finally, the results showed that application of the suggested co-generation system can cover the electricity demand of 294 apartments, heating for 208 apartments, and cooling load for 18,999 apartments.

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Improving the Decision-Making for Sustainable Demolition Waste Management by Combining a Building Information Modelling-Based Life Cycle Sustainability Assessment Framework and Hybrid Multi-Criteria Decision-Aiding Approach

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing the trade-offs among multifaceted criteria. This study develops a BIM-based DWM sustainability assessment approach to facilitate the life cycle sustainability assessment (LCSA) and decision-making by integrating LCSA-related properties and hybrid Multi-Criteria Decision-Aiding (MCDA) methods into a BIM environment using Dynamo visual scripting. A dynamic linkage is developed in the streamlined BIM-based LCSA process, where the enriched Industry Foundation Class (IFC) models are coupled with custom LCSA data templates to achieve seamless data exchange between the BIM platform and external LCA tools. Subsequently, hybrid MCDA methods convert the assessment results into DWM scenario ranking. A pilot study verifies the applicability of the BIM-based framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the ‘status quo’ in China’s recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the ‘growth curve’ of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the “Australian standard” scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, implying that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined settings.