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[Display omitted] • Optimization study of Photo-Fenton and PDS/UV processes for BOD and COD removal. • Photo-Fenton process was able to enhance biodegradability. • PDS/UV process on the best condition only could maintain the biodegradability. • The operational cost of Photo-Fenton processes is much cheaper than PDS/UV process. The aim of this study is to provide an Advanced Oxidation Process (AOP) based solution to enhance biological treatment efficiency by improving biodegradability of wastewater. Photo-Fenton and Peroxydisulfate (PDS)/UV processes were selected in this study as a representative of hydroxyl and sulfate radical based AOPs. A central composition design (CCD) employed to optimize operational cost and BOD/COD ratio to determine optimal conditions for enhancing wastewater treatment efficiency. The study achieved a maximum COD removal of 71.5 % and increased wastewater biodegradability. Optimal conditions for Photo-Fenton process were DOD (H 2 O 2) 0.28, [Fe2+] 106.26 mg/L, and pH 2.32, costing 2.27 USD per 1 kg COD removed. For PDS/UV process, 75.5 % COD removal was achieved at a cost of 10.6 USD/1 kg removed COD. The PDS/UV process had higher COD removal than Photo-Fenton but did not improve wastewater biodegradability. Optimal conditions were DOD (PDS) 0.19, pH 6.68, costing 10.6 USD/1 kg COD removed. [ABSTRACT FROM AUTHOR]

• A Goal Programming model to optimize a waste paper recycling network was elaborated. • The model considers objectives related to sustainability, multiple products and periods. • Vehicle routing, different-capacity vehicles and multiple trips are highlights. • The possibility of storing material at the collection points is considered. • The formulation provides relevant solutions to assist decision-makers. Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. [ABSTRACT FROM AUTHOR]

• Italian consensus about optimization in Nuclear Medicine Therapy. • Optimization must deal with feasibility, limited resources and cost/benefit. • Non-standardized therapies are defined, according to the variability of outcome. • Supremacy of the radiation therapy legislation over the pharmacological laws. • Appeal to EMA to obtain the official acknowledgment of this principle. This recommendation by the Italian Associations of Nuclear Medicine (AIMN) and Medical Physics (AIFM) focuses on the dosimetric optimization of Nuclear Medicine Therapy (NMT) as clearly requested by the article 56 of the EURATOM Directive 2013/59 and its consequent implementation in article 158 in the Italian Law n. 101/2020. However, this statement must deal with scientific and methodological limits that still exist and, above all, with the currently available limited resources. This paper addresses these specific issues. It distinguishes among many possible kinds of NMT. For each type, dosimetric optimization is recommended or considered optional, according to the general criteria adopted in any human choice, i.e. a check of technical feasibility first, followed by a cost/benefit argument. The classification of therapies as standardized or non-standardized is presented. This is based on the complexity of the type of pathology, on the variability of the treatment outcome, and on the risks involved. According to the present document, which was officially delivered to Italian Health Ministry as necessary interpretation of the law, a therapeutic team can, in science and consciousness, overcome the indications of posology, to optimize and tailoring a treatment with dosimetry, on the basis of published national or international data or guidelines, without need of an Ethics Committee approval. Data collected in this way will provide additional evidence about optimal dosimetric reference values. As conclusion, a formal appeal is made to the European and National regulatory agencies for pharmaceuticals to obtain the official acknowledgment of this principle. [ABSTRACT FROM AUTHOR]

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation. • Main process parameters of an industrial papermaking process were identified. • Experimental datasets were achieved during industrial production. • Artificial Neural Networks were trained for process parameters prediction. • Accurate predictions of papermaking process were obtained. [ABSTRACT FROM AUTHOR]

[Display omitted] • Waste office paper was utilized as a substrate for microbial lipid production. • A detailed optimization study has been conducted for biomass and lipid yield. • Optimization results in10.55 g/L biomass, 6.13 g/L lipid with 58% lipid content. • Biodiesel properties of the produced lipids met with the international standards. • A solution to mitigate the hazardous effects caused by waste and fossil fuel. Environmental issues caused by fossil fuel and its rapid depletion demand the need to produce biofuel from renewable energy resources. The disposal of waste paper in landfills causes air pollution and contaminates groundwater. Hence, exploiting paper waste as a renewable resource for microbial lipid production can solve these environmental issues in a greener way. Further, these microbial lipids are a reliable source for biodiesel production. In this study, waste office paper was utilized as a substrate to produce microbial lipids through fermentation by the oleaginous yeast, Rhodosporidium toruloides, using a biorefinery approach. The production conditions were optimized using the Box-Behnken design; a fermentation time of 116.16 h, a C/N ratio of 60, and an inoculum concentration of 9.67% yielded a biomass of 10.55 g/L and a lipid yield of 6.13 g/L with a total lipid content of 58%. The major lipids produced were oleic acid (42%), palmitic acid (36%), stearic acid (9.8%), and myristic acid (8%), which clearly indicates the suitability of the lipids produced by R. toruloides for biodiesel production. The predicted biodiesel properties of the transesterified product met the international standard specifications. Hence, utilizing waste office paper for biodiesel production will be an effective way of mitigating the environmental pollution caused by waste paper and the harvesting of fossil fuels. [ABSTRACT FROM AUTHOR]

Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dissolved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the established integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity consumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs. [ABSTRACT FROM AUTHOR]

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used our software to automatically design 51 different paper microfluidic devices we needed to create a functional lateral flow assay that detects protein and glucose in urine. These designs took only a few seconds to generate and were used in 120 lab experiments we performed in 16 h in the lab. Thus, with the help of our software framework, we went from an idea to a functional device in just two work days. By accelerating device development and enabling researchers without microfluidics experience to create custom devices, our software can help spread paper microfluidic technology to important new application areas. [Display omitted] • Software can automatically generate custom paper microfluidic device designs for a given application. • In just a few seconds, our software generated all 51 device designs we needed to develop a urinalysis assay. • This software enabled us to go from an idea to a functioning paper microfluidic device in just two days in the lab. [ABSTRACT FROM AUTHOR]

Snake robots have redundant kinematic structure which predetermines them for motion in rough and unstructured terrain with obstacles, narrow spaces or environments with different ground levels. Dynamic modeling of snake robots is an important element in the development of control strategies for them. This paper, deals with the dynamic model of a snake robot moving in a pipeline. The model is based on Newton's mechanics with consideration of the viscous as well as Coulomb friction model. The paper uses Hertz theory for modeling of contact between a snake robot and side walls of a pipeline. Based on optimization process results, the friction effects as well as analyses of snake robot propulsive force are investigated. The main contributions of the paper are the developed dynamic model for snake robot motion in a pipeline, open source code of the developed model in MATLAB, and analyses of individual parameters of snake robot motion. • Propulsive force of snake robot moving in a pipeline is significantly affected by friction of the side walls. • One full sine wave formed by snake robot links ensures maximum average velocity. • Considering Coulomb friction the snake robot performs more jerked motion in comparison with viscous friction. [ABSTRACT FROM AUTHOR]

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target. Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δ v impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance. • A Primer Vector Theory-based trajectory optimization tool is shown, tailored for ESA's Comet Interceptor (Comet-I). • The method supports trajectories with multiple revolutions around the Sun and multiple impulses. • The method allows rapid grid search over the search space, finding the lowest cost trajectory and other notable solutions. • A novel analysis of the launch window is presented, contributing to the decision-making process of Comet-I. • A multi-manoeuvre strategy benefits Comet-I's trajectory design, lowering the delta-V cost and improving mission flexibility. [ABSTRACT FROM AUTHOR]

Locomotive engines serve as the powering heart of rail transportation which is widely used for massive cargo over long distances. The traditional locomotive engines have depended on the internal combustion engine for decades, operated using fossil fuels that negatively impact the environment. Since more and more commodities are transported by trains, the prime engines need to be improved where the fuels are expected to enhance the engine's performance and alleviate the environmental impact. This paper presents a comparison of three developed locomotive engines with different configurations, such as an internal combustion engine combined with a gas turbine and molten carbonate fuel cell (ICE-GT-MCFC), a gas turbine combined with a solid oxide fuel cell and onboard hydrogen production using aluminum electrolysis cell (GT-SOFC), and a GT combined with sold oxide and proton exchange membrane fuel cells (GT–SOFC–PEMFC). The paper then identifies one hybridized engine, which is GT–SOFC–PEMFC, according to its low weight and better performance to conduct an optimization study to enhance its efficiency, reduce the cost, and lower the environmental impact. The present optimization study is undertaken through a multi-objective particle swarm optimization, involving thermodynamic, exergoeconomic, and exergoenvironmental analyses. The optimized GT–SOFC–PEMFC locomotive engine is improved in power from 5.9 MW to 7.3 MW and improved in performance to 49.1% energetic efficiency and 47.0% exergetic efficiency. Additionally, the optimized GT–SOFC–PEMFC engine has less specific fuel and product cost of 24.9 $/GJ and 34.7 $/GJ, respectively, and less specific exergetic environmental impact of fuel and product of 13.2 Pt/GJ and 16.2 Pt/GJ, respectively. Also, the relative cost difference and relative environmental impact difference are significantly reduced by more than 20% to 39.4% and 22.8%, respectively. The operational conditions of the optimized GT–SOFC–PEMFC engine are improved to achieve better efficiency and better cost effectiveness in a more environmentally-benign manner. • Comparison between three hybridized combined locomotive engines is established. • Optimized GT–SOFC–PEMFC engine has 7.3 MW power and 49% energetic efficiency. • Specific exergetic product cost declined to 51 $/GJ for fuel and 35 $/GJ. • Specific exergetic environmental impact for fuel and product becomes 13 and 16 Pt/GJ. • The relative environmental impact difference becomes 22.8% instead of 44.3%. [ABSTRACT FROM AUTHOR]

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost. [ABSTRACT FROM AUTHOR]

• A review of waste management optimization models (WM-OMs). 2. • A novel characterization method for examining and benchmarking the features WM-OMs. 3. • A pioneering guide for the selection of proper uncertainty analysis approaches. 4. • A compilation of characteristics of the papers reviewed and their data (Appendix). 5. • A perspective on the design of WM-OMs for different applications and stakeholders. 6. The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of neural style transfer to video sequences, leveraging pre-trained Convolutional Neural Network (CNN) models. By combining content and style losses in an optimization process, the approach transforms video frames into visually captivating compositions, opening new avenues for artistic expression in visual storytelling and filmmaking. The procedure begins with meticulous preprocessing of video frames to align them with the specifications of the powerful VGG16 model. A content model is then constructed to extract activations from the 14th layer of the VGG network, forming the groundwork for nuanced content depiction. Style information is computed by aggregating data from a combination of convolutional layers, resulting in a multifaceted, artistically complex tapestry. The paper introduces a well-balanced loss function that combines style and content losses, serving as the foundation for the ensuing optimization procedure. This careful balancing between style adherence and content authenticity yields visually stunning synthesis, enhancing video sequence quality and artistic resonance. The paper also delves into a detailed analysis of hyper parameters, particularly style weights, and introduces a novel pixel value scaling method for enhanced visual coherence. Experimental findings demonstrate the efficiency and adaptability of the proposed approach, showcasing the seamless integration of content and style for an immersive visual experience. This accomplishment marks a significant stride toward unlocking the creative potential of video content, ushering in a period of enhanced visual storytelling. [ABSTRACT FROM AUTHOR]

Brain stroke is one of the most common causes of disability and death worldwide. In this paper, we present the numerical algorithm behind a new system for brain stroke detection: it relies on a novel imaging technique known as Electromagnetic Tomography (EMT). As compared to classical techniques, EMT has several advantages: it is noninvasive and, since it relies on nonionizing radiation, it is safe for the patient; it can be made compact and portable; it is easy to use and it has relatively low cost. The main drawback of EMT is its relatively poor spatial resolution due to restriction on the frequency spectrum. This paper describes the numerical challenges we had to overcome when designing our latest generation scanner. [ABSTRACT FROM AUTHOR]

This paper investigates an intelligent strut design scheme for achieving high-performance combustion of a combustion chamber under wide-ranging flow conditions. The design process involves optimizing the base strut configuration for the design point and obtaining the Pareto front. The subsequent selection of the optimal configuration is followed by analyzing and interpreting the optimization results using single-factor analysis rules. The findings reveal that the selected optimal configuration enhances the combustion efficiency by 8.22% and reduces the force by 66.12% compared to the traditional DLR combustion chamber. Additionally, a surrogate model is constructed to investigate the influence of the rotation angle of the intelligent strut's rotating blades on the combustion efficiency and force at a Mach number range of 2–3.5. Results indicate that the intelligent strut is capable of improving combustion efficiency via regulation, and the efficiency can quickly reach a desirable level. These findings suggest that the proposed design scheme has potential applications in achieving high-performance combustion in combustion chambers exposed to wide-ranging flow conditions. • This paper investigates an intelligent strut design scheme for achieving high performance combustion. • The design process involves optimizing the base strut configuration for the design point and obtaining the Pareto front. • This design scheme aims to achieve the best comprehensive combustion. Performance based on the design point. • Results indicate that the intelligent strut is capable of improving combustion efficiency. via regulation. [ABSTRACT FROM AUTHOR]

Since the machining of complex aerospace components, like integral compressor-rotors (blade integrated disks), is very cost-intensive, optimizing the process by means of process simulations is an active field of research. With the rise of Quantum Computing, a new instrument with high optimization potential is moving into focus. In this paper, a possible application of Quantum Computing for the machining simulation of multiaxis milling of thin-walled aerospace components is discussed. For this reason, a simulation framework used for the milling simulation is analyzed and each component is evaluated separately in relation to Quantum Computing. Parts of the Harrow, Hassidim, and Lloyd algorithm are proposed to enhance the Finite-Element simulation-based component, like the modal analysis for dynamics simulation. This algorithm can solve linear system problems with exponential speed-up over the classical method. The paper presents a roadmap on how the classical steps of a modal analysis for dynamics simulation could be replaced by quantum algorithms based on quantum phase estimation. The implementation of the first working steps is presented to validate this approach. The linear system problem, arising from the dynamics simulation, is analyzed in detail and a minimal value problem of linear coupled oscillators is derived. [ABSTRACT FROM AUTHOR]

Static mixer studies in literature have often been reported for idealized systems with a concentric injected flow. These do not account for real-world mixing scenarios where the additive comes in through an injector. Depending on the injector geometry, the injected flow, and pipe Reynolds number, the injected flow may end up off-centered. This impacts the static mixer performance as the first few elements are unable to provide efficient mixing. This paper discusses a parametric study of twisted-tape mixers to numerically ascertain their performance for Newtonian flows. It focuses on industrially relevant mixers which operate with some inexactness, e.g., off-center flow of the additive. Effect on mixing of angle of twist and aspect ratio of the individual element along with Reynolds number are studied; and the Reynolds number dependency of mixer geometry for optimal performance is established. This is done without using some oft-used approximations/assumptions in previous literature like zero-thickness mixer walls, 2d analysis, periodic boundary conditions etc. Two non-dimensional quantities, i.e., Coefficient of Variation (CoV) and Z -factor, are used to assess and quantify the operation of the mixers. For idealized systems, increasing the number of elements by reducing the aspect ratio and/or the twist angle is seen to improve mixing for all Reynolds numbers tested (1−100). However, for a real-world mixer constrained by its total length/pressure drop, mixing is proven to be an optimization problem between increasing the number of flow-splits by introducing a greater number of elements and mitigating the anomalous flow (uneven split) caused due to it. For Re ≤ 10, reduction of twist angle from 180∘ to 140∘ improves mixing for only aspect ratios greater than approximately 0.9. For Reynolds number of 100, the 140∘ twist angle outperforms the 180∘ twist angle for all aspect ratios studied. In summary, there is no combination of twist angle and aspect ratio that is a universally optimal solution and these parameters must be decided subjectively based on multiple other input parameters for a given system. • Optimizes the mixer geometry for non-ideal conditions found in manufacturing units. • Establishes a parameter space for twisted tape mixers in terms of Reynolds number, aspect ratio, and twist angle. • Input variables are studied in conjunction with each other instead of in isolation. • Demonstrates the necessity of system-level study, analysis, and optimization of twisted tape mixers. • Concludes that there is no universally applicable or "silver-bullet" geometry that is optimal for all input conditions. [ABSTRACT FROM AUTHOR]

Renewable energy systems (RESs) have played a vital role in meeting the continuously fast-rising energy demand. While RESs provide limitless and ecologically good possibilities, putting RESs in place has proven to be a challenging task due to their intermittent energy supply. Battery energy storage systems have widely been used in microgrids to manage the issue of intermittent energy supply caused by the RESs. The disadvantages of batteries include, large size, limited lifespan, and high cost. For this reasons, energy planners are looking into hydrogen-based storage systems as a potential solution. This paper aims to provide the recent advancements in the sizing and energy management of hybrid RES (HRES), as well as conventional methods. To achieve this, the authors have employed a review strategy that involves several specific tasks, such as preparing a database of articles related to Hybrid RESs with hydrogen storage, designing inclusion/exclusion criteria to filter out relevant articles, and analyzing each article from critical viewpoints. Thus, this paper elaborates on the general formulation framework for optimization, the classification and review of different optimization methods, and the literature on the application and research of Energy Management Systems (EMS). Additionally, the review presents an analysis and comparison of various system optimization methods and EMS through a study of several cases from the literature. It is hoped that the review will act as a basic framework for researchers looking to investigate HRES incorporated with hydrogen storage. • An efficient energy storage system is essential for managing intermittent energy supply. • A fuel cell generates electricity by combining hydrogen and oxygen to form water. • Soft computing techniques use both single and hybrid algorithms. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Species time-histories in shock tube experiments provide rich kinetic information for parameter estimation, but there are two problems in using these data in Bayesian approaches. First, the effective independent-data number is not equal to the number of data points in a curve, so brute multiplication of all data points in likelihood function can weaken the constraints from prior information. Second, taking all points of a curve as targets can lead to results different from that of taking several representative points in the curve. In this paper, we employed maximum a posteriori estimation combined with a neural network response surface to optimize a propane mechanism against multispecies time-histories of propane pyrolysis in a shock tube. Three methods of calculating the likelihood function are used: multiplying all points in a curve (C-160), taking the averaged likelihood in each point (C-1), and taking the likelihood of last points (LastP). The influence of effective independent-data number was studied by comparing C-1 and C-160. It was found that C-160 performed slightly better in fitting experimental data, but brute multiplication overtuned the rate constants beyond a reasonable range. The larger the effective independent-data number, the more severe the overtuning, leading to only a slight improvement of model predictions. The influence of target selection is investigated by comparing LastP and C-1. LastP outperformed C-1 slightly, which can be attributed to the fact that larger discrepancies observed between experimental data and model predictions of the last point can increase the weights of likelihood functions. This further implies that several critical points can represent the entire line for point estimation. This paper can provide a reference both for modelers about reasonable utilization of species time-histories, and for experimentalists about the importance of a detailed probability distribution of measurement error, as well as experiment design with emphasis on critical points. [ABSTRACT FROM AUTHOR]

Satellites engaged in observation missions often face severe pointing constraints, which can limit the ability of their solar panels optimize the generation of electrical power during payload operations. Some approaches to maximize power generation involve either using orientable solar panels, or performing orbital maneuvers to point fixed or deployable (non-orientable) solar panels towards the Sun, which can compromise the payload's ability to meet its pointing requirements. In this study, an alternative approach that enables high-consuming remote sensing payloads to operate for extended periods without requiring orientable solar panels is presented. The satellite under consideration is equipped with fixed or deployable (non-orientable) solar panels mounted on up to three different perpendicular faces. The on-board payload is required to remain within a fixed angle from a target direction, which can be either nadir or a specific geographical position. To derive an optimal tracking law, the projected solar array area at each instant is maximized, resulting in optimal electrical power generation. This methodology allows the satellite to maintain its pointing constraints and efficiently operate high-consuming payloads for longer periods. The proposed tracking law is validated using an actual mission scenario, comparing the power generation achieved by the proposed method against other techniques. The approach presented in this paper shows improved performance, enabling satellites equipped with non-orientable solar panels to fulfill their observation requirements while optimizing electrical power generation. This work offers significant benefits for satellite operators, reducing the need for costly orientable solar panels and enhancing the overall efficiency of satellite missions. • Guidance law for optimal power generation while complying with payload pointing constraints. • Simple and easy formulation, suitable for real-time systems. • Methodology validated and compared against other attitude strategies. • Integration and simulation of the guidance law into a control scheme. [ABSTRACT FROM AUTHOR]

Green hydrogen is central to the global energy transition. This paper introduces a renewable hydrogen production system model that optimizes hydrogen production on a worldwide 50 km × 50 km grid, considering country-specific investment risks. Besides the renewable energy's impact on the hydrogen production system (HPS) design, we analyze the effect of country-specific interest rates on the levelized cost of hydrogen (LCOH) production. Over one-third (40.0%) of all cells have an installed solar PV capacity share between 50% and 70%, 76.4% have a hybrid (onshore wind and solar PV) configuration. Hydrogen storage is deployed rather than battery storage to balance hydrogen production via electrolysis and hydrogen demand. Hybrid HPSs can significantly reduce the LCOH production compared to non-hybrid designs, whereas country-specific interest rates can lead to significant increases, diminishing the relative competitiveness of countries with abundant renewable energy resources compared to countries with fewer resources but fewer investment risks. [Display omitted] • Worldwide levelized cost of hydrogen production on a 50 km × 50 km grid resolution. • Impact of country risk premiums on the levelized cost of hydrogen production. • Cost-optimal hybrid hydrogen production system design per grid cell. • All results are available on an open-data license. [ABSTRACT FROM AUTHOR]

This paper presents a novel, robust, efficient, and simple optimization algorithm called the Object-Oriented Programming Optimization Algorithm (OOPOA) for tackling constrained and unconstrained optimization problems. The algorithm is inspired by the inheritance concept of Object-Oriented programming languages, where the features of a class are classified into three types according to inheritance probability: public, private, and protected. The object-oriented programming inheritance concept is implemented in the algorithm to update the population for the next generations. The proposed algorithm ensures exploitation by selecting the solution with the highest fitness to be inherited in each iteration. It ensures exploration by applying a mutation process that helps explore wide regions in the search space. The performance of this technique is demonstrated by solving 34 different optimization tasks, including 20 standard benchmark problems, ten IEEE Congress of Evolutionary Computation benchmark test functions, and four constrained real-world engineering design problems. [Display omitted] [ABSTRACT FROM AUTHOR]

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i , where 1 ≤ i ≤ 4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO 2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems. • Techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. • Insights to optimal operating conditions, sector coupling potential, and economic feasibility of Power-to-Methanol plants. • Analysis of the impact of market dynamics on the operational efficiency and economic viability of the integrated plant. • Study of the role of intermittent solar power and its impact on the performance and economic sustainability of the plant. [ABSTRACT FROM AUTHOR]

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to optimize traffic networks in a supplier-customer system based on specific strategies of game theory. The traffic network is represented as a routing configuration in which the arcs represent various possible roads between supplier and customer entities, each route having an associated transport cost for the product unit. The game structure is defined by a matrix of transport costs between the players, supplier and customer, with their corresponding strategies. Based on these costs, the game matrix is built to which the existing quantities in warehouses are added, respectively the quantities requested by the final customers. By solving the game associated to the transportation traffic network, the different strategies are proposed for choosing the optimal solution. The simulation results confirm the effectiveness of the proposed approach and recommend some performant procedures for solving the game, such as Hurwicz, Laplace and Savage, close to optimal solution. This research opens new directions for management optimization in other domains such as comunication, electrical and administrativ networks. [ABSTRACT FROM AUTHOR]

In the modern era, industrial automation ofers significant competitive advantages. Replacing human labor with robots allows processes to be carried out much faster and more efficiently, with lower waste rates and the possibility of scheduling production 24/7. However, this also presents new challenges and opportunities in optimizing these automated processes. This paper proposes a hybrid algorithm BRKGA (Biased Random Key Genetic Algorithm) to optimize fexible job shop scheduling (FJSSP) problems in automated manufacturing systems. The algorithm is applied to a real-world case study in the Cinvestav Intelligent Manufacturing Laboratory, seeking to optimize the production of a four-piece product. The results demonstrate that the proposed algorithm outperforms previous results in terms of both, solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a new spherical motion generator and presents a method for modeling its magnetic field and analyzing its moments. The generator employs an electromagnetic drive of a spherical motor as its driving method and utilizes a spherical parallel manipulator to execute the spherical motion. The combination of these two technologies offers several advantages, including a large workspace and high motion accuracy. The equivalent magnetizing current method is used in the magnetic field modeling and the average air-gap flux density is optimized to achieve a better magnetic field distribution, and the accuracy of the analytical model is verified by finite element simulations and experiments. • The electromagnetic-driven SMG merges the spherical parallel manipulator and motor. • A novel SMG is designed to provide large workspace and unlimited spinning motion. • Modeling the magnetic field of SMG as to optimize the magnetic pole arrangement. • The accuracy of the model is verified by finite element method and experiment. [ABSTRACT FROM AUTHOR]

One of the most challenging robotic manipulator designs is finding an appropriate balance between the shaking force and shaking moment because this reduces vibrations. Several approaches have been introduced in the last decades; nevertheless, some assumptions must be established to make such a balance. In this paper, a dynamic balancing approach is proposed. The main novelty is the no dependence on specific trajectories to be executed by the manipulator, which allows finding a design with a similar tradeoff in the balancing under robot configuration changes. Also, the proposal incorporates mass distribution and link shape in a single design procedure. The proposal is stated as a constrained nonlinear optimization problem and applied to a hybrid serial-parallel robotic manipulator. The use of different bio-inspired algorithms and one gradient one in the solution of the balancing problem reveals that differential evolution finds the most suitable design. Besides, comparative simulation results of the obtained design with other design approaches show that the obtained design presents the most suitable tradeoff between the shaking force and the shaking moment when the manipulator executes tasks with different operating velocities. [ABSTRACT FROM AUTHOR]

Hydrogen energy, characterized by its high calorific value and sustainability, represents a secondary clean energy source. It is predominantly stored, transported, and utilized through pipeline networks. However, a comprehensive optimization approach for the design for the ground pipeline network of hydrogen storage is currently lacking. This paper embarks on a systematic exploration of the layout, station type and parameter design for the ground pipeline network of hydrogen storage. Addressing various application perspectives, the study constructs two distinct pipeline network structures: the Star-Star Global Optimization Model (SSGO-Model) and the Star-Tree Global Optimization Model (STGO-Model). Three cases are discussed under six different constraint scenarios. These scenarios focus on layout optimization, variable capacity layout optimization, and the overall optimization of layout and parameters. An overall optimization strategy is proposed to solve the models. A detailed analysis of the iteration process, layout schemes, station and pipeline constraints, investment, and flow parameters of the pipeline network in the optimization results is presented. Through systematic analysis, the correctness and effectiveness of the model are verified, demonstrating the variability of optimization results under different topological structures and constraint conditions. Moreover, when conducting integrated optimization, the total investment is minimized, proving its superiority. The findings demonstrate the applicability of these models in guiding the construction for the ground pipeline network of hydrogen storage, thereby fostering the advancement of hydrogen storage technology. Concurrently, this research contributes significantly to the development of hydrogen energy, offering strategic insights and practical solutions for its efficient deployment. • Integrated optimization model for hydrogen storage ground pipeline network. • The model considers layout, station type and parameter design. • Overall optimization strategy is proposed. • Three cases verify optimization model. [ABSTRACT FROM AUTHOR]

In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flows and which telemetry data are collected, the network-wide visibility and the performance of monitoring applications may decrease. In this paper, we present the in-band network telemetry problem and extend the existing mathematical optimization models of the problem by proposing a new model that computes the routing of network flows. Results show that the new model outperforms existing models in term of network coverage and monitoring applications performance. The results of this work can be useful for network managers and enterprises to gain real-time insights into network performance. [ABSTRACT FROM AUTHOR]

This paper presents research performed on set-based multi-criteria optimization for the preliminary design of slab frame bridges. As a result of immense CO2 emissions emerging from concrete production, there is a need for optimization methods decreasing the volume of concrete without affecting the function of the structure. Previous research suggests a general correlation between CO2 footprint- and cost reduction, due to minimized material use. However, the aspect of buildability may conflict with lessened material, as optimized designs might simultaneously be less buildable. This research aimed to develop an optimization method with respect to the investment cost and environmental impact while also considering buildability cost aspects. The optimization algorithm shows the possibilities of reducing the environmental impact by up to 13.7% for a slightly increased cost of 2.3%. Thus, by implementing optimization procedures in the early stages of the planning process the holistic cost effects related to green solutions can be presented, favoring the choice of sustainable designs amongst clients during tendering procedures. [ABSTRACT FROM AUTHOR]

The necessity for sophisticated decision support in industrial manufacturing has been described manifoldly and simulation approaches as well as optimization methods are continuously developed. This can be especially promising when it comes to decision-making with multidimensional requirements of economic and ecological efficiency. However, due to the required expert knowledge, there is still a gap between theoretically applicable and industrially applied methods. The paper therefore presents results of two use cases as part of the European project ECOFACT where different approaches for the combination of simulation and (heuristic) optimization were applied with industry partners. An overview of recent works and systematic literature reviews of sustainable manufacturing and the approaches for combining simulation and optimization is given. Correspondingly, the investigated use cases of automobile paint shops refer to the reduction of environmental impacts by optimized heating processes and an improved batching process to minimize paint waste. In the first case, setup times for heating color pools and ovens were shifted to avoid load peaks in a calculation time of less than one minute. In the second use case batch size was increased by around 25%. [ABSTRACT FROM AUTHOR]

• Variational Quantum Circuits (VQCs) play a key role in several applications. • VQCs are parameterized quantum circuits trained by using classical optimizers. • The paper proposes to apply memetic algorithms to train VQCs. • The designed memetic algorithm outperforms the state-of-the-art classical optimizers. Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session. [ABSTRACT FROM AUTHOR]

• A novel global optimization algorithm, rime optimization algorithm (RIME), is proposed. • RIME simulates the growth and crossover behavior of the rime-particle population. • The performance of RIME is adequately discussed based on benchmark functions. • RIME has better convergence accuracy and speed compared to other methods. • RIME is applied to solving five practical engineering optimization problems. This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimization methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft-rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs experiments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME 1 1 Source codes of RIME algorithm are also available at: https://codeocean.com/capsule/4472937/tree and https://www.mathworks.com/matlabcentral/fileexchange/124610-rime-a-physics-based-optimization and https://github.com/aliasgharheidaricom/RIME-A-physics-based-optimization. algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html. [ABSTRACT FROM AUTHOR]

The paper proposes the analysis of four different scenarios of renewables penetration (40%, 60%, 80%, and 100%) with the most affordable generators: photovoltaic panels and wind turbines. The synergy of two storage technologies, such as Li-Ion batteries and the hydrogen power-to-power solution (electrolyzer, H 2 tank, and fuel cells), is evaluated to ensure the balance of the power-grid. The paper discusses the numerical Trnsys-based modeling of the smart grid. A detailed performance evaluation of each component, according to operational maps provided by manufacturers, has been performed to meet an electrical load (10 MW peak - 54 GW h annual). The results of the analysis show the crucial role of the H 2 system in the goal of achieving a higher renewable fraction, mainly due to the possibility of seasonal storage without the self-discharge limitation of batteries. In the 100% renewable solution the contribution of H 2 is equal to 30% of the electric load. • The synergy of Li-Ion batteries and hydrogen is evaluated to ensure grid balance. • Hydrogen integration has been evaluated in the path to 100% renewable energy. • The optimum solution is a tradeoff between renewable share and system cost. • A fully renewable system would cost $0.42/kWh with a 30% hydrogen contribution. • Hydrogen power-to-power system is strongly limited by high cost. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns, scarcity of fossil fuel resources, and uncontrolled demand growth have led to upgrading and restructuring existing energy systems. Sustainable multi-energy systems (MESs) will dominate energy production in the future. Integrated management systems must be provided to plan and control the sustainable MESs for optimal operation. Energy hubs (EHs) represent one of the most promising concepts for optimal management systems in sustainable MESs. They can play a great role in moving towards sustainable MES models. This paper provides a comprehensive review of the concepts of EHs and their applications, also benefits gained from the integration of different energies. In addition, the paper discusses the effect of other related options like renewable energy resources, MESs, demand-side management, and energy storage systems. Finally, the paper presents the most common research areas, methods of solving the EHs' associated problems, presents real applications of EHs and explores the future challenges that face the EHs. [ABSTRACT FROM AUTHOR]

Rising energy prices and regulatory requirements are forcing both large manufacturing companies and small and medium-sized enterprises to minimize costs and emissions associated with energy supply. To operate the considered systems more efficiently overall, numerous energy efficiency measures have already been successfully implemented in recent years. This is particularly the case for the cross-industry air conditioning systems that are the focus of this paper. However, due to the growing share of renewable energies and the resulting fluctuating electricity prices and CO2 emissions factors, energy flexibility measures are becoming increasingly relevant. In this context, flexibility measures allow industrial system operators to benefit from these fluctuations, primarily using energy storages and optimized operation strategies. However, the implementation of energy flexibility measures requires high data availability, a good understanding of the system, and flexibility in production planning. To be able to effectively use the existing energy flexibility potentials, air conditioning systems are examined, that contain widely used cross-section technologies. In this paper, a method to enable optimized energy-flexible operation of industrial air conditioning systems is presented. A common feature of manufacturing sites is a similar topology of the thermal supply infrastructure across industries. The complex overall system is divided into several subsystems, e.g., cooling supply system, ventilation system and the production hall. Production processes typically release waste heat into the indoor air of the production hall, which then must be cooled. By accounting for thermal and electrical output and optimizing the operation strategy using mathematical optimization, both operating costs and CO2 emissions associated with energy provision can be significantly reduced. The presented use case reveals that operational cost savings of up to 18 % and a reduction of CO2 emission associated with energy supply of up to 16 % can be realized by optimizing the cooling supply system on its own. [ABSTRACT FROM AUTHOR]

The present paper studies the automated optimization of reconfigurable robot cells to support engineering planning activities. The specific type of reconfigurable robot cells under investigation allows an arbitrary arrangement of functional machine modules on a base plane accessible to the robotic manipulators. The research is motivated by the fast-growing configuration space of reconfigurable robot cells which results from the configurability of the cell layout and the performed workflows. Searching for optimized configurations within this configuration space is an iterative procedure which currently requires lots of manual efforts and expert assessment skills for each iteration. Performing many optimization iterations requires much time, which typically results in only a few performable iterations and thus in suboptimal configurations. To compensate for this, automated support for the underlying planning problem is required. The specific planning problem under investigation concerns finding motion-time optimized layout configurations for fixed workflows. This planning problem was chosen because of its strong association with the productivity of robot cells and the fact that it is the basis for further considerations on the influences of performed workflows. The main contribution of the research is a guideline concerning the decomposition of the mentioned optimization problem. This decomposition concerns the specific formulation of the optimization problem as well as its solving principle. The approach of the paper is to compare combinations of single- and multi-objective problem formulations as well as single- and multi-step solving procedures. Respective variants of evolutionary and conventional algorithms are tested for solving the optimization problems. The experiments are conducted using a specifically developed simulation and optimization framework which supports layout reconfiguration, robot path planning, and time parametrization based on state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Forest Biorefinery Supply Chain (FBSC) design problem is addressed. A general mathematical framework based on a Generalized Disjunctive Programming (GDP) formulation is proposed as an efficient decision-making tool for the optimal FBSC. The approach simultaneously tackles the dynamic capacity allocation and the facilities' co-location, features that are explicitly modelled. The FBSC superstructure promotes the circularity of the network by including flexible processing recipes and the use of multiple biomass residues as biorefineries raw materials as well as paper recycling. The proposed case study copes with the production of paper and biofuels in Argentina, showing the advantage of the integration of biorefineries with the existing forest industries as well as the addition of value to biomass industrial waste and by-products. Additionally, a set of scenarios are considered and tested. By these examples, key features of the proposed approach are highlighted as essential to reach a profitable FBSC. [ABSTRACT FROM AUTHOR]

Clean energy resources will be used more for sustainability improvement and durable development. Efficient technologies of energy production, storage, and usage results in reduction of gas emissions and improvement of the world economy. Despite 30% of electricity being produced from wind energy, the connection of wind farms to medium and large-scale grid power systems is still leading to instability and intermittency problems. Therefore, the conversion of electrical energy generated from wind parks into green hydrogen consists of an exciting solution for advancing the development of green hydrogen production, and the clean transportation sector. This paper presents a techno-economic optimization of hydrogen production for refueling fuel cell vehicles, using wind energy resources. The paper analyses three configurations, standalone Wind-Park Hydrogen Refueling Station (WP-HRS) with backup batteries, WP-HRS with backup fuel cells, and grid-connected WP-HRS. The analysis of different configurations is based on the wind potential at the site, costs of different equipment, and hydrogen load. Therefore, the study aims to find the optimized capacity of wind turbines, electrolyzers, power converters, and storage tanks. The optimization results show that the WP-HRS connected to the grid has the lowest Present Worth Cost (PWC) of 6,500,000 €. Moreover, the Levelized Hydrogen Cost (LHC) of this solution was found to be 6.24 €/kg. This renewable energy system produces 80,000 kg of green hydrogen yearly. • Wind to green hydrogen represents an interesting solution for greenhouse gas reduction. • Three configurations of wind-park stations designed for green hydrogen production are analyzed. • Grid-connected wind park consists of an optimal solution for the wind park hydrogen station. • The Levelized cost of hydrogen production using a 5 MW grid-connected wind park is 6.24 €/kg. [ABSTRACT FROM AUTHOR]

Renewable energy- based multigeneration systems have been proposed as a viable means of achieving a net-zero future as these systems can decarbonize the power and energy sector simultaneously. Although there has been significant development in this research field, the large sizes of the existing systems in literature are a major setback in the adoption/implementation of these systems. Furthermore, the unavailability of solar and wind energy during some hours daily makes solar or wind energy-based multigeneration systems undesirable in many applications. Considering the urgency/importance of attaining a net-zero emission future, this study presents a novel multi-energy system that can bridge the existing gaps in literature. Therefore, the thermodynamics, economic, and environmental analysis novel CO2-based geothermal micro-multi-energy system that is hybridized with a concentrated solar photovoltaic/thermal system and wind turbine is presented in this paper. The proposed energy system presents an innovative method that can be effectively used to decarbonize future energy production. The multi-energy system is modeled to produce electricity, refrigeration effect, space heating, hydrogen, and hot water. The economic study in this paper also analyzed the Levelized cost of electricity (LCOE), Levelized cost of cooling (LCOC), and Levelized cost of hydrogen (LCOH). A multi-objective optimization operation is also carried out on the modeled system in order to determine the optimal exergy efficiency and total product unit cost. The steady-state performance analyses showed that the overall energetic and exergetic efficiencies of the system are 48,61% and 88.31% and these efficiencies can be as high as 51.76% and 95.08% respectively when the system is optimized with reference to exergy efficiency. The LCOE, LCOC, and LCOH at the optimal total product unit cost are 0.04529 $/kWh, 0.004564 $/kWh, and 28.86 $/kg. [ABSTRACT FROM AUTHOR]

• This paper presents a practical tutorial on solving optimization problems via PlatEMO, by means of abundant examples and source codes. • This paper is the first tutorial for the newest version PlatEMO v4.0. • This paper does not go deep into the technical details of algorithms, but aims to enable beginners to use PlatEMO at a low cost, which is much easier to be understood than the user manual of PlatEMO. • This paper is written according to many questions raised by users in the last five years. PlatEMO is an open-source platform for solving complex optimization problems, which provides a variety of metaheuristics including evolutionary algorithms, swarm intelligence algorithms, multi-objective optimization algorithms, surrogate-assisted optimization algorithms, and many others. Due to the problem-independent nature of most metaheuristics, they are versatile for solving problems with various difficulties such as multimodal landscapes, discrete search spaces, multiple objectives, strict constraints, and expensive evaluations, regardless of the fields the problems belong to. Since PlatEMO was published in 2017, it has been used by many researchers from both academia and industry in the computational intelligence community. However, the basic terms and concepts about optimization may confuse practitioners and junior researchers new to metaheuristics. Hence, this paper presents a practical introduction to the use of PlatEMO 4.0, focusing on the procedures of defining problems, selecting suitable metaheuristics, and collecting results. Note, however, that a description of the technical details of metaheuristics is beyond the scope of this paper and interested readers may refer to the cited references. [ABSTRACT FROM AUTHOR]

A novel re-entry trajectory design approach for a winged reusable launch vehicle that satisfies path and terminal state constraints in the height velocity plane is proposed in this paper. A two-phase re-entry trajectory is proposed. A height-velocity plane entry corridor is designed using all path and quasi-equilibrium glide constraints. The proposed method creates the reference trajectory inside the re-entry corridor using three piecewise polynomials, such as linear and logarithmic. A two-parameter search optimization problem minimizes terminal range-to-go error for this reference trajectory generation problem. Improved Search Space Reduction solves this optimization problem. The Improved Search Space Reduction algorithm is compared to the Grey Wolf Optimizer and Particle Swarm Optimisation algorithms to prove its efficacy and superiority. The reference bank angle is modified using feedback linearization and bank reversal logic to create an entry trajectory that meets all path and terminal constraints. The effectiveness of the proposed method has been validated through Monte Carlo simulations that incorporate uncertainties such as variations in initial state, uncertainties in aerodynamics, and atmospheric perturbations. The controller OPAL-RT OP4510 provides bank angle command at every instant based on the current state of the vehicle parameters associated with real-time simulator OP4510. The real-time hardware-in-loop simulation results show that the obtained re-entry trajectory satisfies all path and terminal constraints. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is one of the most interesting issues in recent industrial production. Energy-efficient scheduling is a promising approach to reducing energy consumption in manufacturing systems. However, energy-efficient scheduling is a problem of great interest, especially for the Distributed permutation Flow-Shop Manufacturing Problem (DPFSMP). This paper addresses the critical challenge of energy efficiency in the industry of DPFSMP optimization, where task processing times depend closely on their position in the production line. The objective is to minimize the total energy consumption (TEC) for all machines in distributed workshops. To solve this problem, we propose two efficient constructive algorithms for energy-efficient scheduling of this type of problem with speed-dependent tasks: the Nawaz Enscore and Ham (NEH) based algorithm and the Greedy Random Adaptive Search Procedure (GRASP) based algorithm. We evaluate the performance of the two proposed algorithms on a set of benchmark problems to identify the one with the best performance. The results show that the (NEH) algorithm outperforms the GRASP algorithm in terms of energy efficiency and solution quality. In fact, the proposed algorithms can be used by manufacturers to reduce energy consumption in their production systems. This can help manufacturers save money, reduce environmental impact and improve overall sustainability. [ABSTRACT FROM AUTHOR]

This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the B I 2 R R T ∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) 2 2 https://neurorobotics.net. which is open access and open source integrative simulation framework powered by Gazebo and developed by our team. • The kinematic model of a 9 DOFs mobile-based manipulator is derived. • The tracking control problem with infinity norm and slack variable is formulated. • A two-timescale recurrent neural (TNN) networks optimization scheme is introduced • TNN addresses the hierarchical optimization problem with planning trajectory. • The experiments using B I 2 R R T ∗ are demonstrated in Neurorobotics platform. [ABSTRACT FROM AUTHOR]