Long-term operation optimization of circulating cooling water systems under fouling conditions

Fouling caused by excess metal ions in hard water can negatively impact the performance of the circulating cooling water system(CCWS)by depositing ions on the heat exchanger's surface.Currently,the operation optimization of CCWS often prioritizes short-term flow velocity optimization for minimizing power consumption,without considering fouling.However,low flow velocity promotes fouling.Therefore,it's crucial to balance fouling and energy/water conservation for optimal CCWS long-term operation.This study proposes a mixed-integer nonlinear programming(MINLP)model to achieve this goal.The model considers fouling in the pipeline,dynamic concentration cycle,and variable frequency drive to optimize the synergy between heat transfer,pressure drop,and fouling.By optimizing the concentration cycle of the CCWS,water conservation and fouling control can be achieved.The model can obtain the optimal operating parameters for different operation intervals,including the number of pumps,frequency,and valve local resistance coefficient.Sensitivity experiments on cycle and environmental temperature reveal that as the cycle increases,the marginal benefits of energy/water conservation decrease.In periods with minimal impact on fouling rate,energy/water conservation can be achieved by increasing the cycle while maintaining a low fouling rate.Overall,the proposed model has significant energy/water saving effects and can comprehensively optimize the CCWS through its incorporation of fouling and cycle optimization.

An adaptive machine learning-based optimization method in the aerodynamic analysis of a finite wing under various cruise conditions

Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

Optimization of Extraction Conditions of Pomegranate Peel Polyphenols and Its Protective Effect on Acute Alcoholic Liver Injury in Mice

[Objectives]This study was conducted to explore the optimization of ultrasonic-assisted organic solvent extraction of pomegranate peel polyphenols(PPPs),and to study the protective effect of PPPs on acute alcoholic liver injury in mice.[Methods]The optimal extraction conditions of PPPs were determined by single factor and orthogonal experiments,and an acute alcoholic liver injury model in mice was established.Bifendate was used as the positive control group to investigate the protective effect of low,medium and high doses of PPPs on acute alcoholic liver injury.[Results]The optimum extraction process parameters were followed as 60%ethanol concentration,solid-liquid ratio of 1:40(w/v),extraction temperature of 50℃,and extraction time of 1.5 h,and the yield was 1.42%.The results of animal experiments showed that PPPs could effectively reduce the degree of alcoholic liver injury in mice,reduce the levels of serum alanine aminotransferase(ALT)and aspartate aminotransferase(AST),and reduce the inflammation and necrosis of liver tissue in mice.Meanwhile,the total polyphenols from pomegranate peel also significantly reduced the expression levels of malondialdehyde(MDA),tumor necrosis factor(TNF-α)and interleukin-6(IL-6)in mice,and increased the levels of superoxide dismutase(SOD)and reduced glutathione(GSH)in liver tissue of mice,indicating its antioxidant and anti-inflammatory effects,further illustrating its protective effect on alcoholic liver injury.[Conclusions]PPPs could reduce the expression levels of TNF-α,IL-6 and MDA in mice,and increase the expression levels of SOD and GSH to achieve the protective effect on acute alcoholic liver injury in mice.This study will provide new ideas for the development of new anti-alcoholic liver injury drug resources.

Optimization of Extraction Conditions for Total Flavonoids from Fructus Aurantii Immaturus and Its Anti-UVB Radiation Activity

[Objectives]Optimum extraction conditions of total flavonoids from Fructus Aurantii Immaturus(TFFAI)and its resistance activity to ultraviolet radiation were investigated in present research.[Methods]The optimal extraction conditions of TFFAI were determined by single factor and orthogonal experiments,and the survival rate of TFFAI on HaCaT cells irradiated with UVB rays was investigated.It s antioxidant capacity was determined by ABTS.[Results]The results showed that the highest yield of TFFAI was obtained with 70%ethanol at a solid-to-liquid ratio of 1:50(w/v)and 40℃for 1.5 h by single-factor and orthogonal experiments.Total flavonoids(0.25-1.00 mg/ml)could significantly improve the survival rate of HaCaT cell line.Meanwhile,the maximum absorption peak of TFFAI was found at 283 nm,and in-vitro antioxidant experiment identified that TFFAI had a good clearance rate to ABTS.It suggestes that TFFAI could protect the cells from UVB damage by absorption of UVB rays and anti-oxidation.[Conclusions]TFFAI played a protective role on UVB irradiated cells through UVB ultraviolet absorption and antioxidant pathways.

Response Surface Methodology as an Approach for Optimization of Vinegar Fermentation Conditions Using Three Different Thermotolerant Acetic Acid Bacteria

This study aimed to investigate optimal fermentation conditions of biological acetic acid fermentation for vinegar production. Optimization was performed on 3 acetic acid bacteria strains namely VMA1, VMA7 and VMAO using Response Surface Methodology (RSM). A Box-Behnken-Design (BBD) was achieved with three different independent process parameters involving: fermentation temperature, original alcohol concentration and original acetic acid concentration and one dependent variable (acetic acid yield). The results showed that the mathematical models describe correctly the relationship between responses and factors (F values of the models (p R2 (coefficient of correlation) respectively 0.96, 0.94, 0.98, and adjusted R2 0.95, 0.92, 0.98). The maximum acidity was obtained respectively at fermentation temperatures, original alcohol concentrations and original acetic acid concentrations ranging from [37.5°C - 45°C], [16% - 20% (v/v)], [1.5% - 2% (w/v)] for VMA1, [40°C - 45°C], [14.5% - 20% (v/v)], [1.7% - 2% (w/v)] for VMA7 and [42°C - 45°C], [17% - 20% (v/v)], [1.5% - 2% (w/v)] for VMAO. The use of these acetic strains in the production of vinegar may seriously lead to a decrease or even an ablation of the costs related to the cooling of bioreactors especially in warm and hot countries, in the context of global warming.

An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions

Due to the enormous utilization of solar energy,the photovoltaic(PV)system is used.The PV system is functioned based on a maximum power point(MPP).Due to the climatic change,the Partial shading conditions have occurred under non-uniform irradiance conditions.In the PV system,the global maximum power point(GMPP)is complex to track in the P-V curve due to the Partial shad-ing.Therefore,several tracking processes are performed using various methods like perturb and observe(P&O),hill climbing(HC),incremental conductance(INC),Fuzzy Logic,Whale Optimization Algorithm(WOA),Grey Wolf Optimi-zation(GWO)and Flying Squirrel Search Optimization(FSSO)etc.Though,the MPPT is not so efficient when the partial shading is increased.To increase the efficiency and convergences in MMPT,the Honey Badger optimization(HBO)algorithm is presented.This HBO model is motivated by the excellent foraging behaviour of honey badgers.This HBO model is used to achieve the best solution in GMPP tracking and speed convergence.The HBO methodology is also com-pared with prior P&O,WOA and FSSO methods using MATLAB.Therefore,the experiment shows that the HBO method is performed a higher tracking than all prior methods.

Catalytic Hydrothermal Liquefaction of Water Hyacinth Using Fe3O4/NiO Nanocomposite: Optimization of Reaction Conditions by Response Surface Methodology

This research aimed at optimizing the reaction conditions for the catalytic hydrothermal liquefaction (HTL) of water hyacinth using iron oxide/nickel oxide nanocomposite as catalysts. The iron oxide/nickel oxide nanocomposite was synthesized by the co-precipitation method and used in the hydrothermal liquefaction of water hyacinth. The composition and structural morphology of the synthesized catalysts were determined using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic absorption spectroscopy (AAS). The particle size distribution of the catalyst nanoparticles was determined by the Image J software. Three reaction parameters were optimized using the response surface methodology (RSM). These were: temperature, residence time, and catalyst dosage. A maximum bio-oil yield of 59.4 wt% was obtained using iron oxide/nickel oxide nanocomposite compared to 50.7 wt% obtained in absence of the catalyst. The maximum bio-oil yield was obtained at a temperature of 320°C, 1.5 g of catalyst dosage, and 60 min of residence time. The composition of bio-oil was analyzed using gas chromatography-mass spectroscopy (GC-MS) and elemental analysis. The GC-MS results showed an increase of hydrocarbons from 58.3% for uncatalyzed hydrothermal liquefaction to 88.66% using iron oxide/nickel oxide nanocomposite. Elemental analysis results revealed an increase in the hydrogen and carbon content and a reduction in the Nitrogen, Oxygen, and Sulphur content of the bio-oil during catalytic HTL compared to HTL in absence of catalyst nanoparticles. The high heating value increased from 33.5 MJ/Kg for uncatalyzed hydrothermal liquefaction to 38.6 MJ/Kg during the catalytic HTL. The catalyst nanoparticles were recovered from the solid residue by sonication and magnetic separation and recycled. The recycled catalyst nanoparticles were still efficient as hydrothermal liquefaction (HTL) catalysts and were recycled four times. The application of iron oxide/ nickel oxide nanocomposites in the HTL of water hyacinth increases the yield of bio-oil and improves its quality by reducing hetero atoms thus increasing its energy performance as fuel. Iron oxide/nickel oxide nanocomposites used in this study are widely available and can be easily recovered magnetically and recycled. This will potentially lead to an economical, environmentally friendly, and sustainable way of converting biomass into biofuel.

Optimization of the Conditions for Using Sewage to Cultivate Navicula tenera

[ Objective] This study was to improve the biomass of Navicula tenera, and thus to provide reference for achieving the industrial production of Navicula tenera. [Method] The feasibility of using sewage to cultivate Navicula tenera was preliminarily investigated based on the consideration of regional characteristics; and a series of culture conditions including the nutrient source of nitrogen（N）, phosphorus（P）, iron（Fe）, silicon（ Si ） and the salinity in medium for culturing Navicula tenera, were optimized by single factor test and orthogonal design.[Result] The optimized conditions for cultivating Navicula tenera using sewage are as follows： the water from Xiaoerlou Artificial Lake of Nanjing University of Technology as basic solvent; 360 mg/L urea; 150 mg/L N2HPO4 · 12H2O; 50 mg/L ferric citrate; 2 000 mg/L Na2SiO3 · 9H2O; 2.0 mol/L salinity. Navicula tenera was strongly adaptive to sewage and could well uptake the nutrient sources in the sewage. Under the optimized conditions, the culture cost decreased, and meanwhile the biomass of Navicula tenera reached 4. 766 g/L which is 3.57 multiples over original medium and 1.9 multiples over optimized medium No. 1.[ Conclusion] This study laid a foundation for the combination of culturing Navicula tenera in large scale and sewage treatment.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Optimization of Fermentation Conditions of Acid Resistant α-amylase Producing Strain

[Objective] The aim was to optimize the fermentation conditions of acid resistant α-amylase producing strain. [Method] Based on the selection of an acid resistant α-amylase producing strain,the fermentation conditions including C,N contents and initial pH of culture medium,seed age,inoculum size,rotation speed of shake flask and fermentation temperature were optimized. [Result] The optimum fermentation conditions for acid resistant α-amylase producing strain were：seed age 14 h,inoculum size 8%,initial pH 5.5,fermentation temperature 35 ℃,rotation speed 150 r/min,the volume of inoculum broth 25 ml,C content 1.0% and N content 0.5%. [Conclusion] Under the optimum fermentation conditions,α-amylase activity reached 31.4 U/ml,which was 65.3 % higher than that before optimization.

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural habitat.FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards.The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after feeding.To assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark functions.These functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test suite.FLO’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative analysis.The simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search process.This balanced approach enables FLO to outperform several competing algorithms in numerous test cases.Additionally,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization challenges.Overall,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Screening and identification of an antagonistic bacterium strain LHB02 and the optimization of its fermentation conditions

A strain LHB02 with strong antibacterial activity against some aquatic pathogens was screened from the coastal marine sediment in Fujian province, China. LHB02 was identified as Bacillus subtilis, based on its 16S rRNA sequence, together with the morphological, physiological and biochemical characteristics. The antagonistic activity of strain LHB02 and its optimal fermentation conditions were also investigated. The results showed that LHB02 had strong antagonistic activity against 3 species of vibrios： Vibrio harveyi,Vibrio alginolyticus and Vibrio anguillarum, and the optimum fermentation conditions for the strain LHB02 were as follows： KB culture medium （peptone 20 g, glycerol 10 mL, K2HPO4 1.5 g, MgSO 4 .7H2O 1.5 g, H2O 1 000 mL）; temperature, 28 ℃; pH ,7.0; culture time, 36 h; and inoculation amount, 1.5% （v/v）.

Towards the performance limit of catenary meta-optics via field-driven optimization

Catenary optics enables metasurfaces with higher efficiency and wider bandwidth,and is highly anticipated in the imaging system,super-resolution lithography,and broadband absorbers.However,the periodic boundary approximation without considering aperiodic electromagnetic crosstalk poses challenges for catenary optical devices to reach their performance limits.Here,perfect control of both local geometric and propagation phases is realized through field-driven optimization,in which the field distribution is calculated under real boundary conditions.Different from other optimization methods requiring a mass of iterations,the proposed design method requires less than ten iterations to get the efficiency close to the optimal value.Based on the library of shape-optimized catenary structures,centimeter-scale devices can be designed in ten seconds,with the performance improved by ~15%.Furthermore,this method has the ability to extend catenary-like continuous structures to arbitrary polarization,including both linear and elliptical polarizations,which is difficult to achieve with traditional design methods.It provides a way for the development of catenary optics and serves as a potent tool for constructing high-performance optical devices.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

Optimization on Fermentation Conditions of Orange Vinegar by Box-behnken Design

The optimal process for orange vinegar was investigated in order to explore the effect of process parameters and obtain the superior process parameters.The process for orange vinegar was optimized using single-factor methodology and Box-behnken methodology, and the effect of process parameters was analyzed in the light of response surface plots. Results were as follows. The optimal process of alcohol fermentation was as follows： original sugar degree of 16%, yeast inoculum size of 5% and p H value of 3.5 through single-factor experiment. The optimal fermentation process of acetic acid optimized by response surface methodology was as follows： inoculum size of 8.52%, original alcohol content of 6.0% and p H value of 3.3, in the optimal condition the yield of acetic acid was 53.11 g/L. Single factor action and the interaction of two factors were presented on the yield of acetic acid.The effects of inoculum size and p H were extremely significant, and that of original alcohol content significant. The interaction of inoculum size and original alcohol content was non-significant, that of inoculum size and p H also non-significant. On contrast, original alcohol content and p H had significant interaction.

Multi-Material Topology Optimization for Spatial-Varying Porous Structures

This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.