Optimal Settings Research Articles

Direct contact membrane distillation for surface water treatment and phenolic compounds retention: From bench to pilot scale

Membrane distillation has been applied in water treatment for micropollutant retention, such as phenols. Direct contact membrane distillation (DCMD) is the most studied configuration at the bench scale. Translating lab-scale results to large-scale experiments requires attention. In general, by combining both experiment's results, optimal settings, and process conditions can be better determined. Thus, the DCMD scaling up for water treatment and phenols retention was evaluated. As expected, permeate flux reduced considerably from bench to pilot scale (15.8 ± 0.8–2.9 ± 0.1 kg m−2 h−1) due to the inflow energy limitations, membrane compaction, and temperature polarization. Increasing the permeate recovery (50–70 %) did not affect the permeate flux and the compound's retained in both systems. The pilot system was validated with different surface water matrices (14.2, 92.5, and 162 NTU), proving the DCMD robustness. In all operational conditions, the permeate reached the water quality guideline required for human consumption demonstrating the technology consistency and reliability. A preliminary economic analysis, considering three different scenarios (distributed system for communities, mobile system, and polishing for conventional WTP), showed the DCMD as a relatively competitive technology for the application in drinking water polishing (US$ 0.50–0.19/m3).

An application of hybrid Taguchi-ANN to predict tool wear for turning EN24 material

This work is an attempt to predict tool wear for turning EN24 material by the hybrid Taguchi-ANN (Taguchi-Artificial Neural Network) method. The objective is to minimize the tool wear. The independent factors are cutting environment, feed rate, depth of cut, nose radius, and tool type. A Spinner numerical control lathe is used to assess performance. As per the Taguchi orthogonal array, 27 experiments are conducted for each value of the uncontrollable factor (spindle vibration). Optimal setting is structured by Taguchi analysis and the response table. The additive model is used to predict the response. Conformity test is carried out to check whether the predicted and experimental values of response are within the range given by the confidence interval. Furthermore, the ANN is used to predict and analyze the tool wear. The result showed that the supremely important parameter is depth of cut and the least important parameter is tool type. The ideal set found is A3, B3, C3, D1, and E3. Through ANN analysis, it is observed that the experimental values are very close to the predicted values of tool wear. The predicted value at optimal setting is 0.0401 mm. The experimental values at optimal setting is 0.0422 mm. In addition, the study showed that when the feed rate and nose radius are both set to high levels and the depth of cut is medium, using an uncoated tungsten carbide tool with minimal lubrication results in the least amount of tool wear.

Prediction of trunk muscle activation and spinal forces in adolescent idiopathic scoliosis during simulated trunk motion: A musculoskeletal modelling study

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness). The present study investigates the effect of three different lumbar spine rhythms and absence/presence of disc stiffness on trunk muscle imbalance in the lumbar region and on intervertebral lateral shear at different levels of the thoracolumbar/lumbar scoliotic curve, during simulated trunk motions in the three anatomical planes (flexion/extension, lateral bending, and axial rotation). A spine model with articulated ribcage previously developed in AnyBody software and adapted to replicate the spinal alignment in AIS subjects is employed. An existing dataset of 100 subjects with mild and moderate scoliosis is exploited. The results pointed out the significant impact of lumbar spine rhythm configuration and disc stiffness on changes in the evaluated outputs, as well as a relationship with scoliosis severity. Unfortunately, no optimal settings can be identified due to lack of reference validation data. According to that, extreme caution is recommended when aiming to adapt models of adult individuals with physiological alignments to adolescent subjects with scoliotic deformity.

A SOCP-Based ACOPF for Operational Scheduling of Three-Phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

Human‐in‐the‐loop optimized rocker profile of running shoes to enhance ankle work and running economy

AbstractIncreasing the efficiency at which muscles generate mechanical power could improve running economy. A potential way to reduce muscle fiber shortening velocities and enhance energy storing of the Triceps Surae is changing their gear ratio at the ankle via optimization of shoe rollover profile. The aim of the current study was to individually optimize rollover profile of rocker shoes via human‐in‐the‐loop optimization to maximize positive ankle work to redistribute joint work from the hip and knee to the ankle and improve running economy. A total of 10 runners ran on a treadmill with experimental rocker shoes in which apex position and angle were optimized using an evolution algorithm to maximize positive ankle work. We compared experimental shoes with optimal settings, standard settings, and control shoes in terms of biomechanics and running economy. Optimal apex parameters differed considerably between participants. The optimal condition resulted in higher positive ankle work and a higher proportional share of the ankle in the total positive lower limb work compared to the standard condition. A difference in running economy between these conditions was not found. Human‐in‐the‐loop optimization can redistribute joint work from the hip and knee to the ankle by individually optimizing apex parameters. Although this did not improve running economy, the study showed that human‐in‐the‐loop optimization could improve the effectiveness of footwear with respect to the selected optimization parameter on an individual level.

Utilization of iron fillings solid waste for optimum biodiesel production.

This study explores the innovative application of iron filings solid waste, a byproduct from mechanical workshops, as a heterogeneous catalyst in the production of biodiesel from waste cooking oil. Focusing on sustainability and waste valorization, the research presents a dual-benefit approach: addressing the environmental issue of solid waste disposal while contributing to the renewable energy sector. Particle size distribution analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray fluorescence (XRF), Thermal analysis (TG-DTA), and FTIR analysis were used to characterize the iron filings. The response surface methodology (RSM) was used to guide a series of experiments that were conducted to identify the optimum transesterification settings. Important factors that greatly affect the production of biodiesel are identified by the study, including catalyst loading, reaction time, methanol-to-oil ratio, reaction temperature, and stirring rate. The catalyst proved to be successful as evidenced by the 96.4% biodiesel conversion efficiency attained under ideal conditions. The iron filings catalyst's reusability was evaluated, demonstrating its potential for numerous applications without noticeably decreasing activity. This work offers a road towards more environmentally friendly and sustainable chemical processes in energy production by making a strong argument for using industrial solid waste as a catalyst in the biodiesel manufacturing process.

Potential energy interpolation with target-customized weighting coordinates: application to excited-state dynamics of photoactive yellow protein chromophore in water.

Interpolation of potential energy surfaces (PESs) can provide a practical route to performing molecular dynamics simulations with a reliability matching a high-level quantum chemical calculation. An obstacle to its widespread use is perhaps the lack of general and optimal interpolation settings that can be applied in a black-box manner for any given molecular system. How to set up the weights for interpolation is one such task, and we still need to diversify the approaches in order to treat various systems. Here, we develop a new interpolation weighting scheme, which allows us to choose the weighting coordinates in a system-specific manner, by amplifying the contribution from specific internal coordinates. The new weighting scheme with an appropriate selection of coordinates is proved to be effective in reducing the interpolation error along the reaction pathway. As a demonstration, we consider the photoactive yellow protein chromophore system, as it constitutes itself as an interesting target that bears long-standing questions related to excited-state dynamics inside protein environments. We build its two-state diabatic interpolated PES with the new weighting scheme. We indeed see the utility of our scheme by conducting nonadiabatic molecular dynamics simulations with the required semi-global PES based on a limited number of data points.

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Web browsing is an important application domain, but it imposes a significant power burden on mobile devices. While the heterogeneous multi-core design offers the potential for energy-efficient computing, existing web browsers fail to exploit the hardware to optimize mobile web browsing. Our work aims to offer a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict the optimal processor setting for rendering the web content. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the Chromium browser and testing it on a representative big.LITTLE mobile platform. We apply our approach to the top 1,000 hottest websites across seven typical networking environments. Our approach achieves over 80% of the performance delivered by a perfect predictor. Our approach achieves over 30%, 50%, and 60% improvement respectively for load time, energy consumption and the energy delay product when compared to two state-of-the arts approaches.

Effect of Laser Process Loops on the Hole Diameter and Hole Formation of Laser Micro Drilling on TC4

Laser micro drilling stands as a precise manufacturing method that employs a focused laser beam to craft accurate, small holes within a diverse array of materials. Its applications span across vital industries like aerospace, medical, and electronics, playing a pivotal role in creating components like fuel injectors, medical implants, and microelectronics. Within this context, a notable challenge emerges in obtaining a refined surface finish during laser micro drilling. This study delves into the impact of a laser loop, a crucial parameter, on the surface quality of TC4, also known as Ti6Al4V—an aerospace staple. Employing a Conventional Fiber Laser with a peak output of 30 W, the experiment meticulously directs the laser beam onto the TC4 surface via a microscope objective lens. The drilling process unfolds in controlled conditions, mitigating external variables such as temperature and humidity. Assessment of drilled hole surfaces transpires through both light and 3D microscopes. Interestingly, holes subjected to higher laser power and increased laser loop rates demonstrate enhanced surface smoothness. In essence, this inquiry demonstrates the substantial influence of laser loop on TC4’s surface finish during laser micro drilling. Elevating the laser loop factor leads to heightened surface refinement and diminished roughness in drilled holes. It was found that the diameter entry of the micro-holes was increased by 61% - 89.35% and the diameter exit of the micro-holes also increased by 55.55% - 62.79%. The outcomes of this investigation offer valuable insights for refining the laser micro drilling process to achieve premium surface quality on TC4 and comparable materials. As such, these findings extend guidance for optimal laser loop settings in the realm of laser micro drilling across various materials, benefiting future manufacturing endeavors.

DEVELOPMENT AND PERFORMANCE OPTIMIZATION OF ECM PARAMETERS ON SCRAPPED ALLOY WHEEL METAL MATRIX COMPOSITES

Electrochemical machining (ECM) is a nontraditional technique for application of metal matrix composites in aerospace, military, and other engineering uses. ECM factors such as voltage, electrolyte concentration, duty cycle, and electrolyte temperature are varied to evaluate the machining rate. radial overcut, and delamination factor on scrapped alloy wheel MMC. The entropy-based weight assessment method coupled with gray relational analysis is used to perform multi objective optimization. As per the entropy method the weight assigned to MR, ROC and DF is 0.3105, 0.3432 and 0.3463, respectively. The gray relational grade predicts that 8 V, 20 g/lit, 50% and 40°C is the optimal setting for higher machining rate and lower radial overcut and delamination factor.

Erosion rate investigation and multi-objective optimisation on electrical discharge machining of Ti64 using deep cryo-treated electrodes

Due to its inherent properties, Ti64 has set many challenges in industries during its machining using conventional machining processes. Traditional machining of Ti64 is not a feasible option considering the economic and environmental perspectives. Non-conventional techniques like electrical discharge machining (EDM) are promising methods for machining Ti64 despite challenges like higher tool wear and lower machining rates. The current paper investigated the erosion rates while machining Ti64 using deep cryo-treated electrodes. The peak current (Ip), pulse on time (Ton) and gap voltage (Vg) were used as control parameters while investigating erosion rates, viz., Material Removal Rate (MRR) and Tool Wear Rate (TWR). Experiments were planned according to the RSM-based Central Composite Design (CCD). Considering the multi-objective nature of the current problem, Grey Relational Analysis (GRA) has been incorporated to find a single parametric condition that will satisfy both MRR and TWR. Peak current, [Formula: see text][Formula: see text]A, Pulse On time, [Formula: see text][Formula: see text][Formula: see text]s and Gap voltage, [Formula: see text][Formula: see text]V was found to be the optimal setting giving [Formula: see text][Formula: see text]mm3/min and [Formula: see text][Formula: see text]mm3/min. Furthermore, topographical analysis of the EDMed surfaces has been performed using SEM, XRD and 3D optical profilometry techniques to characterise the surface and to investigate the possibility of other intermetallic compounds formed on the machined surface.

Adapting grain crop seed threshing equipment for processing sesame for streamlining the mechanisation of post-harvest operations

The process of threshing is crucial in separating seeds from harvested plants. Kimseed has recently developed the Multi-thresher CW09(b) for research labs and seed vendors. This study aimed to evaluate the effectiveness of Kimseed’s threshing equipment for sesame and determine the optimal setting parameters. Laboratory experiments were conducted with varying levels of rotor speed and blade angle. The Kimseed multi-thresher proved effective for sesame when the rotor speed was between levels 6 to 8, and the blade angle was between levels 1 to 2. The highest capacity of 15.39 kg/h was achieved at the speed of level 8 and the blade angle of level 3. Blade angle level 1 resulted in better threshing efficiency than blade angles 2 and 3, with the threshing efficiency above 97%.

Evaluation and Optimization Based on Exergy in Kamojang Geothermal Power Plant Unit 3

The quality of production well from the Kamojang geothermal power plant unit 3 diminishes annually, whereas there has been a substantial rise in the demand for electrical energy in the region. This research focuses on optimizing the vacuum pressure in the main condenser by employing exergy analysis, a methodology grounded in the principles of the second law of thermodynamics. Exergy analysis offers insights into each system component's exergy efficiency and irreversibility. Furthermore, an energy assessment is conducted to offer insights into each component's energy consumption or utilization. Energy and exergy rates are computed for every state and component within the power plant, encompassing the steam receiving header, separator, demister, turbine, main condenser, inter condenser, after condenser, and cooling tower. The exergy analysis findings reveal that the exergy rate derived from the production well amounts to 95327 kW, generating 52882 kW of electricity and producing a system exergy efficiency of 55.47%. The turbine experiences the highest irreversibility, totaling 12874 kW. Adjustments are made to the main condenser vacuum pressure to optimize the system, aiming to identify the optimal setting that maximizes both exergy efficiency and power output. The optimization outcomes indicate that reducing the vacuum pressure in the main condenser leads to enhanced exergy efficiency and increased power output. The optimal vacuum pressure obtained is 0.1 bar, resulting in the highest exergy efficiency and output power of 57.42% and 54738 kW, respectively, with the lowest irreversibility of 32751.07 kW.

Evaluation and Optimization Based on Exergy in Kamojang Geothermal Power Plant Unit 3

The quality of production well from the Kamojang geothermal power plant unit 3 diminishes annually, whereas there has been a substantial rise in the demand for electrical energy in the region. This research focuses on optimizing the vacuum pressure in the main condenser by employing exergy analysis, a methodology grounded in the principles of the second law of thermodynamics. Exergy analysis offers insights into each system component's exergy efficiency and irreversibility. Furthermore, an energy assessment is conducted to offer insights into each component's energy consumption or utilization. Energy and exergy rates are computed for every state and component within the power plant, encompassing the steam receiving header, separator, demister, turbine, main condenser, inter condenser, after condenser, and cooling tower. The exergy analysis findings reveal that the exergy rate derived from the production well amounts to 95327 kW, generating 52882 kW of electricity and producing a system exergy efficiency of 55.47%. The turbine experiences the highest irreversibility, totaling 12874 kW. Adjustments are made to the main condenser vacuum pressure to optimize the system, aiming to identify the optimal setting that maximizes both exergy efficiency and power output. The optimization outcomes indicate that reducing the vacuum pressure in the main condenser leads to enhanced exergy efficiency and increased power output. The optimal vacuum pressure obtained is 0.1 bar, resulting in the highest exergy efficiency and output power of 57.42% and 54738 kW, respectively, with the lowest irreversibility of 32751.07 kW.

Scanning Electron Microscope Analysis of Polypropylene Filter Cartridges Used in Drinking Water Purification Systems

In recent years, more and more emphasis has been placed on the use of home filtration systems as a coarse pre-filtration step. The PP (polypropylene) filter cartridge is one of the most common of these systems, with the role of retaining solid suspensions from drinking water. However, few studies have focused on the fouling analysis of PP cartridges using EDS (Energy-Dispersive X-ray Spectroscopy) analysis methods. Through this study, a clear and in-depth view of the structures and morphology of PP filter cartridges as well as their impurity retention capacities and their impact is provided with the help of an SEM (Scanning Electron Microscope) analyzer. To achieve these goals, it was necessary to establish a specialized preparation methodology for this type of material in order to analyze it using the SEM and, at the same time, determine the optimal setting of the SEM parameters (improved resolution, reduced acceleration voltage, reduced data acquisition time, etc.) depending on the analysis performed for the visualization and detailed characterization of surfaces. Based on the SEM-EDS analysis and characterization, an uneven distribution of impurities on the surface of the PP fibers was identified. The number of impurities varied according to the depth of the cartridge due to the sieving effect that occurred owing to the varied sizes and shapes of the impurities, but also the structural differences and pore sizes of the filter material. So, the most common chemical elements identified were Al, Si, Na, Cl, Ca, Fe, and S, having a predominantly higher intensity from the inside to the outside of the PP filter cartridge due to pressure forces and the uneven flow of filtered water.

Performance optimization and investigation of metal-cored filler wires for high-strength steel during gas metal arc welding

Abstract This study examines the utilization of metal-cored filler wire in conjunction with the gas metal arc welding (GMAW) technique for welding high-strength S690QL steel. Since welding parameters significantly impact the bead quality and weld joint integrity, the main objective was to identify the optimal welding parameters. To achieve this, the input variables including the current (A), voltage (V), and gas flow rate (GFR), and their effects were evaluated for reinforcement (R), width (W), depth of penetration (DOP), and the width of the heat-affected zone (HAZ). For a more efficient and cost-effective investigation, a Box–Behnken design, which is based on response surface methodology, was used for bead-on-plate trials. Mathematical regression models, derived from experimental data, were rigorously validated using the analysis of variance, main effects plots, residual analysis, and the R 2 and Adj. R 2 values. Additionally, the heat transfer search (HTS) algorithm was employed for process optimization. While single-objective optimization provided optimal settings for individual responses, simultaneous optimization aimed to strike a balance between multiple, sometimes conflicting, objectives. This comprehensive approach resulted in specific values, including a reinforcement (R) of 4.285 mm, a width (W) of 9.906 mm, a DOP of 2.039 mm, and an HAZ width of 2.020 mm. These values were achieved with specific input parameters: current (221 A), voltage (24 V), and GFR (21 L·min−1). The Pareto solutions offered a nuanced selection of the most suitable configuration, taking into account the desired values for R, W, DOP, and HAZ. The close alignment between predicted and experimentally measured values for the responses highlights the precision and suitability of the HTS algorithm in estimating critical bead geometries during GMAW of S690QL plates.

Model-based many-objective optimization for control parameters of underwater glider considering long-term high-quality CTD measurements

Currently, the optimization of underwater gliders (UGs) primarily focuses on improving the platform performance, while ignoring the incidental influence on onboard observations. In this study, a model-based many-objective optimization framework is first introduced to acquire the optimal control parameters of UGs, which enables efficient energy utilization and quality-assured conductivity, temperature, and depth (CTD) observations. Different from prior studies only emphasizing platform performance, our framework innovatively takes both CTD measurement errors and sampling errors into the optimization of control parameters, presenting a novel standpoint for achieving energy-efficient observations of interest within the dynamic ocean system. To further improve optimization in local environments, the dynamic model of UG in our framework was also upgraded through the integration of near real-time environmental information, facilitated by the Douglas–Peucker algorithm. Within the context of this framework, we investigated the requirements for conducting CTD observations in the forward and depth directions of glider movement and analyzed the optimal control parameters settings for these scenarios, encompassing both regular and segmented control strategies. The results suggest that conducting CTD observations during UG's climbing phase, rather than its diving phase, yields advantages. Otherwise, upgrading the buoyancy system and implementing segmented control strategy would be necessary. The thorough assessment in our study demonstrates the potential and versatility of the proposed framework in providing the optimal control parameters options for enhancing long-term high-quality CTD measurement by UGs. We believe that it will provide valuable guidance for glider operation in future field experiments.

Optimization of duplex heat treatment settings with the MOORA-PCA technique to customize the mechanical and wear response of Ti6Al4V alloy

The impact of duplex heat treatment settings was examined to optimize the mechanical and wear responses of Ti6Al4V alloys. A multi-objective optimization using ratio analysis (MOORA) coupled with PCA was used to optimize the different outcomes. Heat treatment aims to reduce the wear rate, frictional coefficient, and improve tensile strength and microhardness. Variables like solution treatment temperature, duration, aging temperature, and time are considered for optimization. The optimization results reveal that the optimal circumstances are A3B1C2D3. The appropriate process parameter combination was finally evaluated by a confirmation test, which shows that the MOORA-PCA technique worked well together to find the best heat treatment settings. All of the heat-treated samples demonstrated better mechanical and wear characteristics. However, compared to other heat-treated samples, samples that underwent heat treatment in optimal settings have superior features. Compared to the heat-treated samples at trial 9 and the optimum conditions, the samples at trial 8 settings showed 5.4% and 12.3% lower microhardness, respectively. The alloy subjected to optimal conditions for heat treatment has a tensile strength of 1154.87 MPa, which is 12.95% and 6.09% greater than the specimen heat-treated at trials 8 and 9, respectively. Compared to the as-received alloy, the sample that underwent heat treatment at optimal settings exhibited a wear rate of approximately 40% lower than the as-received alloy, 3% lower than the heat treatment settings used in trial 8, and nearly 26% lower than the heat treatment settings used in trial 9. The bi-model microstructure developed under optimal circumstances is correlated with increased mechanical and wear properties.

Analysis of methods of classification of electronic messages based on neural network models

In the article, the creation of a mechanism for detecting and classifying messages is considered, with an assessment of how effectively different neural networks work and can recognize and classify different types of electronic messages, including phishing attacks, spam, and legitimate messages. A preliminary analysis of incoming messages has been performed, encompassing their headers, text, and other relevant attributes. For instance, in the case of emails, these attributes could be the 'subject' and 'sender' of the message. Methods for data preparation and processing have been reviewed, including text vectorization, noise removal, and normalization, to be utilized in training neural networks. Message tokenization has been performed by transforming them into a numerical format while considering the selection of features. For text messages, it is crucial to execute both tokenization and text vectorization. The model training was performed on the test data with prior splitting into two parts: 80% for training and 20% for testing. The training set is utilized for training the model, while the test set is used to evaluate its effectiveness. The peculiarity of the class structure of the data, namely the uniformity of the distribution of classes, is considered. In this case, spam occurs less frequently than legitimate messages, so class balancing techniques such as random deletion of redundant examples, upsampling, and subsampling were applied to ensure adequate model training. Optimization of network parameters was performed, by researching the optimal parameters of neural networks, such as the number and size of layers, activation functions, and optimization of hyperparameters to achieve the best performance. Hyperparameter optimization includes determining optimal settings for neural networks, such as layer size, activation functions, learning rate, and other parameters. The effectiveness was assessed by comparing the results and performance of various classification methods based on neural networks using metrics such as precision and F1-score. It was determined how well the methods can avoid misclassifications where legitimate messages are mistakenly identified as spam, and vice versa. A comparison of the methods' effectiveness in processing a large volume of messages in real time was conducted. An analysis of different architectures of neural network models was performed. Based on the analysis, it was revealed how effectively different neural network models can recognize and classify messages as spam.

A novel asynchronous time-scale holistic control method for heating system based on the energy state space

Optimal characteristic parameters and control strategies in the operation of urban heating systems are significant for carbon reduction and high efficiency. Considering load fluctuations and real-time outdoor temperature changes, this research constructs steady-state and dynamic heat current models of the heating system based on the standard thermal resistance model. Combined with genetic algorithms, an optimal setting method is proposed for the operating characteristic parameters of the heating system under variable operating conditions. To achieve the control of the optimal characteristic parameters, the dynamic heat current model is applied to derive the system energy state space equations and obtain the state space parameter matrix. Moreover, a holistic control strategy for the heating system based on the optimal node parameters is proposed. The control simulation results show that the proposed holistic control strategy based on the optimal node temperature can save energy by 25.4 % compared with the fixed node temperature. Meanwhile, the exergy efficiency of the heating system increases by 2.3 %. That is, the proposed asynchronous time-scale holistic control method is significant for the day-ahead optimal regulation strategy of the district heating system.