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This paper presents an overview of pulp and paper mills (PPM) production processes, the resulting release of wastewater effluent loaded with wide range of pollutants and associated environmental impacts. The review highlighted the different types of functional materials and their modified forms employed as coagulants for pulp and paper mills industries effluent (PPME) treatment that have been intensively studied as a promising strategy for PPM to achieve cleaner and sustainable treatments in accordance with sustainable development goals (SDGs) "6-Clean water and sanitation", "9-Industry, innovation, and infrastructure", and "12-Responsible consumption and production". Standalone coagulation treatment processes are inherently ineffective towards meeting the increasingly stringent discharge requirements, coupled with their higher energy demand, and increased operational and maintenance costs. Owing to the recalcitrant nature of PPME contaminants, this review explored the effectiveness of the coagulation processes for decontamination of PPME. Furthermore, the review provides a state-of-the-art coagulation-based hybrid systems employed for enhanced PPME treatment. The process limitations, influencing factors and optimization techniques are highlighted. The review also highlights how sustained research in the subject area impacts on achieving cleaner production. The review also discusses coagulant classifications and the synergistic, antagonistic and shock load toxic effects of hybrid coagulants on toxicant biodegradation and their associated system efficiency. Moreover, it offers a guide for the development and application of sustainable hybrid-based coagulants for PPME treatment. The findings presented herein provide a vital theoretical foundation for sustainable solutions to improve coagulation-based hybrid systems efficiency and their scale-up towards potential commercialization. [Display omitted] • Pulp and paper mills produce effluents containing diverse and emerging contaminants. • Different materials and their modified forms are used as coagulants for PPME treatment. • The synergistic and shock load toxic effects of hybrid-based coagulants could be identified. • Hybrid systems are highly efficient techniques for PPME treatment. • The review highlights process weaknesses, influencing factors and optimization techniques. [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 presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

The necessity of employing renewable energy sources within microgrid-based Power-to-X (P2X) systems has been emphasized by the imperatives of reducing global warming and fossil fuel consumption. Due to their numerous advantages, solar and wind energy have acquired prominence in contemporary power systems. However, their intermittent nature presents challenges. To overcome these challenges, energy storage devices are utilized. Batteries, however, are plagued by issues such as high maintenance needs, self-discharge, and diminishing storage capacity with age. Consequently, investigating alternatives such as hydrogen storage has become a financially viable option, especially for high-power applications. However, the intrinsically slow dynamics of hydrogen production from electricity contrast with the rapid responses of contemporary power electronic devices. This paper presents a versatile technique for optimizing hydrogen production in the presence of stochastic fluctuations in solar irradiance and wind speed. The proposed method combines a modified version of the Symbiotic Differential Whale Optimization Algorithm (m-SDWOA) with power converters to maximize production from renewable sources and electrolyzers. Using extensive simulations to demonstrate the efficacy of the proposed method by comparing its performance to that of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The results demonstrate the superior performance of the m-SDWOA-integrated method and pave the way for improved hydrogen generation in microgrid-based P2X systems. • Innovative control framework coordinates WT, PV, BESS, and HESS. • Maximizes hydrogen production while maintaining power quality. • Efficient controllers stabilize DC-bus voltage amidst WT/PV variability. • Ensures consistent, reliable power supply in microgrid. • Efficient energy management via m-SDWOA for resource optimization. [ABSTRACT FROM AUTHOR]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]

The paper entitled “Experimental investigation of machinability characteristics and multiresponse optimization of end milling in aluminium composites using response surface methodology (RSM) based grey relational analysis (GRA)”. The Authors adopted RSM based grey relational method to achieve high productivity in terms of maximum material removal rate MRR and least surface roughness, tool wear and cutting forces. Experimental design was made using RSM (central composite design) and responses were measured according to standard methods. In this study, authors had proposed the optimized Grey relational grade (GRG) ranking for machinability characteristics. However, there are some errors in their analysis, which are likely to affect the significance of their proposed ranking. So, this comment highlights the miscalculations and gives the corrected results. [ABSTRACT FROM AUTHOR]

The increased application of Information and Communications Technology in automation and digitization is the driving concept towards the actualization of the Industrial Revolution 4.0. Many aspects of industrial design and operations including offshore engineering have been automated. However, the selection procedure of mooring design parameters including azimuth angle, pretension, diameter, fairlead slope, and mooring radius, which is a critical aspect of mooring system design has remained heavily based on a manual approach despite the availability of sophisticated hydrodynamic analysis software. Up to date, there is no proper review on optimisation techniques that have the potential of automating these procedures. In the past, some of the few available mooring optimisation procedures consider only the mooring lines for the prediction of optimal platform offset without due consideration to the integrity of the risers. Thus, the significance of adopting an integrated riser-mooring design methodology in the optimisation of mooring design variables is discussed in this paper where the integrity of the riser is adequately represented using a Safe Operation (SAFOP) zone polar diagram. We also review the developmental transition from the use of classical to Evolutionary Algorithms (EA) in the optimisation procedure. EA has been acknowledged as a practical alternative for solving mooring optimisation problems. Hence, some of the EA techniques are presented and their computational capabilities explored. In the last part, a concise review of the application of optimisation techniques for mooring optimisation of the different offshore floating platforms is presented based on their capabilities in minimizing platform offset and efficiency in terms of simulation time. So far, EA techniques like the Differential Evolution (DE), variants of Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) have been used to optimize mooring line design variables. Based on the discussions, the potentials of DE, RegPSO and other variants of PSO can be further explored for better efficiency. • Previous studies addressing differential evolution (DE), genetic algorithm (GE) and particle swarm optimisation (PSO) techniques used for optimisation of mooring parameters have been critically reviewed. • The integrated riser -mooring design methodology is effective in accounting for integrity of riser and mooring systems. • The application of evolutionary algorithms, particularly the variants and hybrids of DE ,PSO and GA, have proven to be efficient optimisation of mooring systems. • Some variants of PSO like the RegPSO have demonstrated improved capability in terms of escaping premature convergence , thereby resulting in better computational efficiency. [ABSTRACT FROM AUTHOR]

To improve convergence speed of deep neural network trained by gradient descent methods (GDMs), this paper proposed a novel fractional order gradient descent optimizer (FOAdam) based on Caputo operator and Adam algorithm, and analyzed the impact of fractional order on convergence performance using optimization theory and simulation. Furthermore, to address the trade-off between convergence speed and precision under fixed-order conditions, a fractional order scheduler (FOS) was designed based on the Connections Cloud Model (CCM). Numerical experiments conducted on MNIST and CIFAR-10 datasets showed that FOS-FOAdam outperformed other mainstream GDMs in terms of convergence speed and precision. Finally, FOS-FOAdam was applied in recognizing and classifying image datasets of cuttings and cores at the logging site, demonstrating good prospects for practical engineering applications. [ABSTRACT FROM AUTHOR]

Partially reusable launch vehicle design has raised a great interest in order to minimize the costs of space transportation by recovering and refurbishing the first stage. Several configurations such as toss-back architectures are now operational. These concepts require additional rocket propellant in order to carry the boost-back and landing maneuvers, that induce several losses in terms of payload mass performance for the ascent mission. In order to limit these losses, this paper focuses on the design of two types of architectures to carry out the return-to-launch-site mission. The first, named glide-back, uses the main propulsion system of the first stage to perform a boost-back burn and returns to the landing site by a gliding mode using additional lifting surfaces. The second, named, fly-back, performs the return-to-launch-site mission using several air-breathing engines located in the nose of the first stage combined with lifting surfaces. In this paper, the design of a reusability kit, allowing to provide the first stage with both expendable and reusable capabilities, is investigated. This kit is composed of the lifting surfaces, the nose including the air-breathing propulsive system (for fly-back configuration), landing gears and additional avionics. This paper addresses the design of such reusability kits and presents the mission specifications, the design process relying on Multidisciplinary Design Optimization techniques, the vehicle performance and the optimal trajectories. Different analyses (aero-propulsive, optimal control, operational considerations, etc.) are detailed and trade-offs between the studied configurations are assessed. [Display omitted] • Reusable launch vehicles. • Multidisciplinary design and optimization of reusability kit. • Inclusion of aeronautical technologies in launch vehicle design. [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]

Oilfield wastewater with high moisture content can be efficiently treated by supercritical water gasification (SCWG) technology to produce hydrogen. This paper conducted a thermodynamic and environmental analysis of the oilfield wastewater SCWG system to provide optimization strategies. The waste heat utilization technology can enhance the heat integration of the system to reduce the exergy destruction and increase the H 2 yield. Sensitivity analysis showed that a ratio of preheating water to emulsion 3 can promote water-sensitive reactions to produce H 2 and save the H 2 needed to maintain the energetically self-sufficient system. Low system pressures, high feedstock preheating temperatures, and low oxidation reactor temperatures can reduce system energy destruction to obtain high H 2 yield and system efficiency. Meantime, the developed SCWG system has the potential to achieve natural enrichment of greenhouse gases after the H 2 separation has been completed. The use of waste heat utilization technology and carbon capture and storage (CCS) technology is expected to increase system efficiency to 53.43 % and reduce global warming potential (GWP) of 5.52 kg CO 2 -eq/kg H 2. This work would be of great value to the process design and optimization for the SCWG system of oilfield wastewater. [Display omitted] • The process of supercritical water gasification of oilfield wastewater was simulated. • Optimization strategies were given by thermodynamic and environmental analysis. • Operational details were revealed by comprehensive sensitivity analysis. • The exergy efficiency of 53.43 % were obtained. • Global Warming Potential decreased to 5.52 kg CO 2 -eq/kg H 2 with CCS. [ABSTRACT FROM AUTHOR]

• The negative stiffness inertial amplifier tuned mass dampers is introduced. • The new closed-form expressions for optimal design parameters of NSIA-TMDs. • The H 2 and H ∞ optimization based closed-form expressions for optimal design parameters. • NSIA-TMDs shows 50% better vibration reduction capacity than conventional TMD. The negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) are introduced in this paper. Another two novel tuned mass dampers such as negative stiffness tuned mass damper (NS-TMD) and inertial amplifier tuned mass damper (IA-TMD) are mathematically developed from the negative stiffness inertial amplifier tuned mass dampers (NSIA-TMD) and the static masses of these three novel dampers are retained constant. The exact closed-form expressions for optimized system parameters for these novel dampers are obtained using H 2 and H ∞ optimization techniques. The dynamic responses of the SDOF systems controlled by H 2 and H ∞ optimized novel tuned mass dampers subjected to base excitations are obtained analytically. The dynamic response reduction capacities of the novel tuned mass dampers are compared with the dynamic response reduction capacities of traditional tuned mass dampers (TMD). Therefore, the dynamic response reduction capacities of H 2 optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 45.51 % , 43.47 % , 41.08 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NS-TMD, NSIA-TMD, and IA-TMD are significantly 3.31 % , 8.98 % , 13.79 % superior to the H ∞ optimized traditional tuned mass dampers. The nonlinear negative stiffness inertial amplifier tuned mass dampers (NNSIA-TMD) are also introduced in this paper. As a result, the dynamic response reduction capacities of H 2 optimized nonlinear negative stiffness tuned mass damper (NNS-TMD), NNSIA-TMD, and nonlinear inertial amplifier tuned mass damper (NIA-TMD) are significantly 24.54 % , 21.92 % , 19.12 % superior to the H 2 optimized traditional tuned mass dampers. Furthermore, the dynamic response reduction capacities of H ∞ optimized NNS-TMD, NNSIA-TMD, and NIA-TMD are significantly 3.01 % , 9.04 % , 15.08 % superior to the H ∞ optimized traditional tuned mass dampers. The outcomes of this research are mathematically accurate and relevant to practical design applications. [ABSTRACT FROM AUTHOR]

Space-filling designs are useful for exploring the relationship between the response and factors, especially when the true model is unknown. The wrap-around L 2 -discrepancy is an important measure of the uniformity, and has often been used as a type of space-filling criterion. However, most obtained designs are generated through stochastic optimization algorithms, and cannot achieve the lower bound of the discrepancies and are only nearly uniform. Then deterministic construction methods for uniform designs are desired. This paper constructs uniform designs under the wrap-around L 2 -discrepancy by generator matrices of linear codes. Several requirements on the generator matrices, such as a necessary and sufficient condition for generating uniform designs, are derived. Based on these, two simple deterministic constructions for uniform designs are given. Some examples illustrate the effectiveness of them. Moreover, the resulting designs can be regarded as a generalization of good lattice point sets, and also enjoy good orthogonality. • The deterministic construction methods for uniform designs under the wrap-around L 2 -discrepancy are given. • A necessary and sufficient condition for generating uniform designs are derived. • The resulting designs can be regarded as a generalization of good lattice point sets, and also enjoy good orthogonality. [ABSTRACT FROM AUTHOR]

• A novel full nonlinear mathematical model of asymmetric hydro turbine governing system established. • The multi-stability characteristics of as well as its generation mechanism are revealed. • The effect mechanism of water diversion system topology on the system stability is revealed. • The recommended setting values of system parameters under different topologies are provided. This paper aims to study the stability and nonlinear dynamics of hydro-turbine governing system with asymmetric water diversion system, i.e. asymmetric hydro turbine governing system, by using Hopf bifurcation theory. Firstly, the full nonlinear mathematical model of asymmetric hydro turbine governing system is established by all system components and nonlinear head loss. This model contains two units with different capacities which share a common pipeline. Based on the nonlinear mathematical model, the multi-stability characteristics of asymmetric hydro turbine governing system under load disturbance is studied by using stable domain and verified by numerical simulation. Moreover, the multi-time scale oscillation is revealed and its relationship to system multi-stability is investigated. Furthermore, the effect of system parameters and topological parameters on system stability is analyzed. Results indicate that: two stable domains are emerged under load disturbance, the overall stability of asymmetric hydro turbine governing system is determined by the intersection area of the two stable domains. In addition, the system parameters and topological parameters both have obvious effect on system stability, which can be significantly improved by reasonable tuning of system parameters and optimization of water diversion system layout. [ABSTRACT FROM AUTHOR]

This paper considers the completion problem of a partially observed high-order streaming data, which is cast as an online low-rank tensor completion problem. Though the online low-rank tensor completion problem has drawn lots of attention in recent years, most of them are designed based on the traditional decomposition method, such as CP and Tucker. Inspired by the advantages of Tensor Ring decomposition over the traditional decompositions in expressing high-order data and its superiority in missing values estimation, this paper proposes two online subspace learning and imputation methods based on Tensor Ring decomposition. Specifically, we first propose an online Tensor Ring subspace learning and imputation model by formulating an exponentially weighted least squares with Frobenium norm regularization of TR-cores. Then, two commonly used optimization algorithms, i.e. alternating recursive least squares and stochastic-gradient algorithms, are developed to solve the proposed model. Numerical experiments show that the proposed methods are more effective to exploit the time-varying subspace in comparison with the conventional Tensor Ring completion methods. Besides, the proposed methods are demonstrated to be superior to obtain better results than state-of-the-art online methods in streaming data completion under varying missing ratios and noise. [ABSTRACT FROM AUTHOR]

Accurate and reliable wind speed forecasting is of great significance to the management and utilization of wind energy. An improved deep learning model for wind speed forecasting, abbreviated as MODWT-RF-IGWO-LSTM, is presented in this paper. Firstly, the maximum overlap discrete wavelet transform (MODWT) is applied to denoise the original wind speed series. Secondly, the random forest (RF) algorithm is used for feature selection. Thirdly, the improved grey wolf optimization algorithm (IGWO) is applied to optimize the parameters of the long short-term memory (LSTM) model. Finally, the denoised wind speed data is entered into the well-trained LSTM model to obtain the final wind speed forecasting result. The performance of the proposed model is assessed by actual wind speed data for three different months of the year. The experimental results show that the proposed deep learning model for wind speed forecasting has good predictive ability. And the proposed model performs better than other benchmark models in this paper. [ABSTRACT FROM AUTHOR]

Over the past few decades, a considerable increase in the number of space debris has become a serious threat to the safety of the operating satellites. So, collision avoidance maneuvers have become indispensable in providing immediate and cost-effective solutions. The unavoidable presence of uncertainty associated with any physical system adds to the challenge. In this paper, a method is presented to determine the fuel-optimal collision avoidance trajectory using a convex optimization technique while incorporating the evolution of uncertainty associated with the space objects. The safety of the satellite is ensured by the collision avoidance constraints which involve calculating the instantaneous collision probability and using the Mahalanobis distance. An avoidance region is also demarcated around a space object with uncertain states to prevent any collision. Posing the problem as a convex optimization problem helps to eliminate the problem caused by dependency of the optimization variables on the initial guesses used. However, convexification of this problem with evolving uncertainty and collision probability calculation presents a set of challenges. The evolution of the positional error covariance ellipsoid leads to a feasible non-convex space surrounding the avoidance region. Moreover, implementation of a convex optimization formulation here requires convexification of the expression for collision probability, which is originally not purely convex in nature when a Gaussian probability density function is involved. This paper presents an approach to tackle the above-mentioned challenges. The satellite is returned to its nominal orbit in a timely manner, as time spent off the nominal trajectory can often lead to disruption of the space mission. The effects of various parameters on the required Δv are also explored. • Collision avoidance maneuver is formulated as a convex optimization problem. • Collision probability calculation and nonconvex feasible region are convexified. • The optimization includes evolution of uncertainty in the states of space objects. • An approximated Mahalanobis distance leads to a more conservative approach. • The effects of orbital, control and uncertainty parameters are explored. [ABSTRACT FROM AUTHOR]

The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive framework for analysing embodied carbon in TBM tunnelling. • Evaluate module-based carbon outputs with functional units via BIM-driven platform. • Baseline surveys for segmental lining designs and carbon reduction strategies. • Establish correlations between geotechnical parameters and TBM carbon emissions. • Identify limitations of current assessment methods suggesting a holistic framework. Alongside the transition towards sustainable and renewable infrastructure and energy production, tunnelling projects that adopt tunnel boring machines (TBM) continue to increase in size and complexity. Life Cycle Assessment (LCA) methodologies have a significant impact on the Architecture, Engineering and Construction (AEC) industry, with a shift in focus to cost and carbon management across the infrastructure lifecycle through the use of digitalisation techniques and sustainability rating systems. This paper aims to develop a systematic framework to enhance the sustainability of underground infrastructure in the early design and construction stages, aiming for "build clever and efficiently". This framework integrates digitalisation, carbon assessment standards, numerical modelling, and optimisation techniques to assess carbon emissions and establish benchmarks for construction products and processes. The approach uses parametric models to define tunnelling-related entities such as geological formations, TBM, and segments, designed with dimensional constraints that allow for adaptive modelling and interactions. Enabled by building information modelling (BIM) through parametric models enriched with domain properties, module-based carbon accounting is adopted to evaluate the footprint of material- and asset-level key emitters with functional units. Leveraging established databases, baselines for segmental lining designs and carbon reduction strategies are established. Through project-driven case studies, the correlations among geotechnical conditions, tunnel dimensions, and TBM operating parameters are developed to quantify TBM carbon emissions. A prototype programme demonstrates the framework's application on typical metro and road tunnels at different levels of details (LoD). This paper serves as a guideline for conducting embodied carbon assessments specifically tailored to TBM tunnelling, acknowledging the substantial contribution of the TBM construction process, which is controlled by geotechnical conditions. [ABSTRACT FROM AUTHOR]

• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization. • A comprehensive review of underwater image enhancement and restoration methods is provided. • The publicly available underwater image datasets and underwater image quality metrics are introduced. • The extensive experiments on underwater image enhancement and restoration methods are conducted. • The challenges in the field are discussed and the promising insights for future directions are provided. [ABSTRACT FROM AUTHOR]

• Importance and challenges of relay coordination in Microgrid. • Overview of different adaptive schemes opted for relay coordination. • Different optimization techniques which have been used for the calculation of relay settings. • Evolvement of algorithms from conventional to Hybrid methods which combines two different metaheuristic methods to solve a complex, non- linear optimization problem in an efficient way for optimized relay coordination. • Future directions of research for further modifications possible in Algorithms are stated at the end. Concept of microgrids (MGs) have gained significant attention in order to enhance the resilience and efficiency of modern Power Systems. MG offers bi-directional power flow so an effective protection and control becomes a vital requirement to ensure the reliable operation of MGs. Relay coordination being an integral part of the protection system, plays a critical role in prevention of power disturbances and equipment damage due to the presence of bi-directional power flow. This review paper focuses on the optimization of relay coordination in MGs, and highlights the importance of adaptive and intelligent approaches involved for the efficacy of MG operation. This paper overviews different schemes used to adapt while determining the DOCRs settings with different contingencies considered. The paper depicts the key challenges associated with relay coordination in MGs; varying fault currents, and bidirectional power flows. To overcome these challenges, recent researches have explored advanced techniques that includes intelligent algorithms to operate on collected real-time data. Additionally, the impact of communication technologies for relay coordination in real-time has been discussed for Grid connected and Islanded mode of operation. This review covers a wide range of optimization methods such as; Traditional, Meta-Heuristic, and Hybrid algorithms, which allows adaptive and reliable protection schemes during different modes of MG operations. [ABSTRACT FROM AUTHOR]

• Novel FADSS is fabricated for the water production from thin air. • It incorporates a small blower to facilitate airflow through the system. • Condenser is used for the condensation of desorbed vapour from silica gel. • Bed design study is performed for effective solar energy utilization. • Honeycomb bed, with an airflow rate of 30 LPM, is found as optimal. This paper presents an innovative approach addressing the problem of clean and drinking water scarcity by developing and optimizing a flow-assisted desiccant solar still (FADSS) that harnesses atmospheric moisture using solar energy. It is an extension of the conventional desiccant solar still incorporating a small blower to facilitate airflow through the system, and operates in a two-cycle process. During the night (adsorption process), ambient air is blown continuously through the bed, enabling the silica bed to adsorb moisture. During the daytime (desorption process), solar energy heats the silica bed, releasing the adsorbed water vapour to the circulating air, which carries it to the condenser. The water vapour condenses in the condenser, and the air returns to the bed. The paper focuses on optimizing the FADSS by studying the airflow rate, a critical parameter for maximum water production. It varies from 20 to 60 L per minute (LPM). Further, the paper also investigates the four different bed design configurations for maximum water production. The FADSS thermal efficiency is calculated to analyze its performance. The highest water production of 694 ml/day was achieved using a 0.25 m2 glazing area in the honeycomb bed design. This output was obtained by employing an airflow rate of 30 LPM and a 3 cm thickness of silica gel. Interestingly, The honeycomb bed effectively utilized ∼38 % of solar energy for water production in the FADSS, while about 9 % was lost due to sensible cooling of the air in the condenser. The findings suggest that the honeycomb bed, with an airflow rate of 30 LPM, represents the optimal operational parameters for this system. [ABSTRACT FROM AUTHOR]

• A method for extending polarimetric optimization of PS-InSAR and SBAS techniques on dual polarized Sentinel-1 data is proposed. • The phase quality and density of coherent pixels in both techniques are improved with the proposed method. • The potentials of the proposed method for differential land covers are investigated. The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data. [ABSTRACT FROM AUTHOR]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

This paper proposes an H ∞ / H ∞ optimization technique to distributed fault detection (FD) for multi-agent systems (MASs). To simultaneously consider the effects of unknown disturbances and faults in the MASs and event-triggered transmission errors (ETTEs) on the residual signal, a residual generator related to the event-triggered threshold coefficient is designed. The generated residuals achieve the best compromise between robustness to unknown disturbances and sensitivity to faults. The obtained results have a better detection performance by using H ∞ / H ∞ optimization technique compared with other optimized schemes. In addition, the applicability of the proposed H ∞ / H ∞ optimized distributed FD scheme is verified by the simulation of a vehicle lateral dynamic system. [ABSTRACT FROM AUTHOR]

Deploying shallow geothermal solutions is critical for meeting energy demands while supporting decarbonisation targets. In densely populated areas, drilling large numbers of boreholes may lead to thermal interactions between closely located borehole heat exchangers. This paper presents a novel method termed the infinite borehole field model to estimate the technical shallow geothermal potential, especially in urban regions. The thermal interactions between boreholes are considered using finite element models simulating the operation of a single borehole in a larger field. Mathematical optimisation is used to find the amount of thermal energy that can be annually extracted while keeping the borehole wall temperature above freezing point of water. The method considers thermogeological details of geological formations including downward-increasing ground temperature, geothermal heat flux, thermal conductivity, heat capacity, porosity, density, and advective heat transfer. Results of our case study indicate that 100 m deep thermally independent boreholes can produce 14.20 MWh/a for 50 years on average. However, boreholes in an infinite borehole field spaced 20 m apart produce 7.80 MWh/a. A further investigation including advective heat transfer indicated that high velocity groundwater flow can significantly enhance borehole yield. Our method provides a generalised approach which can be beneficial prior to detailed site investigations. [ABSTRACT FROM AUTHOR]

In this paper, a distributed fixed time gradient algorithm for neurodynamic systems is proposed for solving optimization problems with local inequality constraints. The algorithm is designed using fixed time theory and sliding model control techniques, where each agent has a local objective function known only to itself, and the optimal solution of each local objective function sum can be obtained in a fixed time by the information interaction between neighbors under the condition of local inequality constraints. In addition, the upper bound of the fixed time can be obtained and it is proved theoretically that the upper bound of the fixed time is independent of the initial value. Finally, the stability and effectiveness of the algorithm are verified by numerical examples. [ABSTRACT FROM AUTHOR]

Identification of the geometric errors of rotary axes is important for deciding the machining accuracy of five-axis platforms. In this paper, a programmable identification method is proposed to decouple the position-dependent and position-independent geometric errors (PDGEs and PIGEs) of the rotation axis. First, the kinematics model of the five-axis platform is developed by dual quaternions theory, and then the physical significance of left multiplication and right multiplication of PDGEs error model is analyzed. The motion difference between the base coordinate system and its local coordinate system is compared by simulation analysis. The geometric relationship between rotation direction and motion elements is established based on the error model, which is used to design an optimization algorithm for identifying the orientation errors of PIGEs. Additionally, the effect of linear axis PIGEs is eliminated by equivalent replacement, and a sample regression model is developed to identify the offset errors of PIGEs. The PDGEs are separated considering the Motion First principle. Finally, the proposed model and identification method were verified on a five-axis dispensing platform. • Unifying the kinematic model of five-axis platform with dual quaternions. • Analyzing the differences between different geometric error models in published literature. • Introducing visual technology for error measurement of rotary axes. • A programmable decoupling method for the geometric error of the rotation axis is proposed. [ABSTRACT FROM AUTHOR]

This review provides a deep analysis of the different methodologies to improve the operation of solar thermal plants based on mathematical optimization. The various schemes found in the literature to determine the optimal operational strategy are classified depending on two criteria: time dependence (static or dynamic) and with feedback or not from the plant (real-time or offline). This review shows that offline dynamic optimization is performed on solar thermal plants in research papers but highlights the lack of real-time optimization studies. The analysis work conducted in this review, based on studies of the operation of solar systems but also on process engineering research articles, shows that dynamic real-time optimization seems capable of handling the intermittency of the solar radiation and well suited to improve the operation of a solar thermal plant. Indeed, the daily and seasonal variations of weather and heat demand associated with the uncertainty of their forecasts make the operation of such systems very challenging. This paper details the different ways to implement Dynamic Real-Time Optimization, and the possible improvements to the classical scheme. Perspectives on the application of Dynamic Real-Time Optimization in association with a planning phase to plan a smart use of storage are described. Although it has not been studied in depth in the literature, the Dynamic Real-Time Optimization of a solar thermal plant including storage should be investigated in order to maximize the benefits from the heat sold, extend the time period where the heat demand is met and reduce the consumption of back up fossil fuels. • A literature review of control and optimization of solar thermal plants is provided. • There are challenges associated with weather and demand variations and forecasts. • Dynamic Real-Time Optimization schemes are presented with examples. • Dynamic Real-Time Optimization is well-suited to optimize the solar plant operation. • A planning phase should be added for a better storage management. [ABSTRACT FROM AUTHOR]

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs. [ABSTRACT FROM AUTHOR]

The paper proposes the optimal placement of distributed generators (DGs) in radial distribution system along with reconfiguration. The DGs which are considered in this paper are designed with photo voltaic (PV) cells and wind power generation. The paper studies on the PV and wind generation penetration in radial distribution system as per the irradiation and wind velocity throughout the day. The irradiation levels are considered throughout the day. Unlike in former hybrid optimization techniques, the placement of DGs and reconfiguration is shared between two optimization techniques. The paper also details the reliability improvement of radial distribution system with reconfiguration and DGs placement. Some part of reconfiguration and DG locations are selected with one optimization technique and rest of reconfiguration and DG size are selected with another optimization technique. The combination of two optimization techniques properties leads towards the novel hybrid optimization. The methodology is formulated with the black widow algorithm and crow search algorithm. The performance of the method is tested on two test systems which are 16 bus RDS and 33 bus RDS. [ABSTRACT FROM AUTHOR]

• A niching concept is inspired to enhance the original ChOA. • RN-ChOA proposes a novel constraint handling technique. • The RN-ChOA is applied to estimate parameters on the SM55, SW255, and KC200GT. • RN-ChOA is compared with ten well-known algorithms. Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models' parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models' parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model's parameters. The findings demonstrate the robustness and excellent performance of the suggested approach. [ABSTRACT FROM AUTHOR]

Both temperature control and humidity control are important in buildings. However, usually only temperature is controlled in small and medium-sized buildings using direct expansion (DX) air-conditioning (A/C) systems, while dehumidification is a by-product of cooling, and indoor humidity is in an uncontrolled state. Weights-based fuzzy logic control algorithm (WBFLCA) is a novel fuzzy control algorithm that can be applied to DX A/C systems with good simultaneous temperature and humidity control performance. However, the setting of weight values for WBFLCA is still highly dependent on expert experience and lacks a systematic optimization theory, which hinders the application of WBFLCA. In order to establish a weights setting rule for WBFLCA used in DX A/C systems, a coupled control simulation model is built in this paper to simulate the effect of simultaneous control of temperature and humidity with all 16 basic weights distribution mode combinations. All the 16 combinations are also experimentally tested on an experimental DX A/C system to reveal the control performance of different combinations in a real system. Four of them with good control performance are found. The reasons why these four combinations are good are also analyzed in this paper. Based on the simulation and experimental results, the weights setting rule of WBFLCA applicable to the simultaneous temperature and humidity control of DX A/C systems are summarized. The approach proposed in this paper of finding appropriate pattern combinations from the basic weights distribution patterns has the potential to be applied to solve other decoupling control problems as well. [ABSTRACT FROM AUTHOR]

With the aim of improving passive safety protection in subway vehicles, this paper investigates a cutting energy-absorbing structure by means of experiment and simulation. The proposed structure comprises a cutting knife, an energy-absorbing tube, and a support tool. The Johnson-Cook material model is used to study the coupled thermal–structural cutting energy-absorbing process, and a finite-element model of the cutting energy-absorbing structure is validated using data from a cutting collision experiment. The sensitivity of each factor is discussed using extreme difference analysis to identify the effects of the cutting depth, cutting width, and cutting knife rake angle on the energy absorbed (EA) and the peak cutting force (PCF). Based on sample points obtained from an optimal Latin-hypercube experimental design, a relationship between the crashworthiness index and the design variables is formulated using polynomial-response-surface and radial-basis-function surrogate models. Through comprehensive error analysis, the most accurate surrogate model is used to optimize the crashworthiness of the structure. The multi-objective particle swarm optimization algorithm is applied to multi-objective optimization to obtain the maximum EA and minimum PCF. The weighting factor plays an important role in selecting the ideal Pareto solution, and the optimal solution is determined using the minimum distance selection method. The results allow us to determine the structure with the best crashworthiness, for which EA = 991 kJ and PCF = 1441 kN. • Present a cutting energy absorption structure for used in subway vehicles. • The coupled thermal–structural method is used to analysis the crashperformance of the cutting absorption energy structure. • The results of simulations are in good agreement with the experimental results. • The multi-objective optimization of cutting energy absorption structure is carried out by two surrogate models. • The MOPSO algorithm is used to obtain the optimal parameters of the cutting energy absorption structure. [ABSTRACT FROM AUTHOR]