Optimization Model Of Process Research Articles

Optimizing negotiation process of buyer-supplier collaboration in the gambier supply chain

Abstract The current gambier supply chain presents the disparity issue in profit distribution between the business actors. This issue could be analyzed regarding the collaboration that exists between the involved parties, particularly in the negotiation process. The present study aims to develop an optimization model of the negotiation process between supplier (farmers) and collector (buyer) in gambier supply chain. In a decentralized supply chain setting, the linear programming is applied to develop the optimization planning model for both players. Optimized models were built to align the order level of buyers and the supply quantity of suppliers. The proposed negotiation process is applied iteratively to align the order and supply quantity. The developed models have been verified through computational testing using both real and estimated data. Practical implication of the research results can assist the related actors in the gambier supply chain.

Energy consumption optimization model and system for the pellet drying process of straight grate

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control.

Process Optimization and Kinetic Modeling for Sucrose Acylation to Sucrose-6-acetate through the Dibutyltin Oxide Method

Process Optimization and Kinetic Modeling for Sucrose Acylation to Sucrose-6-acetate through the Dibutyltin Oxide Method

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Design of an improved process optimization model for enhancing the efficiency of the wastewater treatment process

ABSTRACT Efficient wastewater treatment is crucial for environmental protection and ensuring the availability of potable water. However, existing optimization models often fall short in effectively analyzing historical data, optimizing dosages, or predicting optimal process parameters. This paper aims to address these challenges by proposing an advanced process optimization model that enhances the efficacy of wastewater treatment processes. Our objectives include the development of a novel method for optimizing various aspects of the treatment procedure using Auto Encoders (AE), Genetic Algorithm (GA), and Vector Autoregressive Moving Average (VARMA) models. Extensive experimentation with multiple datasets and samples, including the Melbourne Wastewater Treatment Dataset, Urban Wastewater Treatment, and Global Wastewater Treatment, was conducted to validate our model. The results demonstrate significant improvements in our proposed model, with an average improvement of 8.5% in treatment quality, a 4.9% reduction in delay, and a 9.5% increase in water purity compared to recently proposed models. We conclude that our model provides a valuable framework for optimizing wastewater treatment in a variety of settings due to its adaptability and effectiveness. Future research could explore the integration of additional advanced techniques and real-time monitoring systems to further enhance the model's precision, efficiency, and robustness.

Integrated optimization of crude oil procurement planning and blending scheduling for property stabilization

Crude oil procurement and blending are key processes in refinery production. When solving the integrated optimization problem of simple oil procurement and blending, the following issues are mainly considered: how to establish an integrated optimization model of crude oil procurement and blending process under different time scales to minimize the cost of procurement under the premise of ensuring the stability of the properties of blended crude oil. The paper proposes an integrated optimization model, takes cost minimization as the model's objective function, and provides stability through the yield and property constraints of blended crude oil. Then, the paper describes the procurement process by an event-based representation and the blending process by a continuous event representation and proposes a hybrid event-time-based representation to describe the model. Finally, this paper verifies the model's effectiveness in solving real production problems through case simulations.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Simulation and optimization model for mechanical biological treatment biodrying process: validation with experimental results

Simulation and optimization model for mechanical biological treatment biodrying process: validation with experimental results

Research on the Design of Power Supply Gateway and Wireless Power Transmission Based on Edge Computing

There is a rising need for sensors under an IoT network to identify and monitor the environment as more IoT devices and services are made accessible for use. This movement presents challenges such as the proliferation of data and the scarcity of energy. This research presents a strategy for enhancing the service provision capabilities of WSN-aided IoT applications by combining mobile edge computation with wireless signal and control transmission. In order to reduce overall system energy consumption while maintaining data transmission rate and power needs, a new optimization problem integrating power allocation, CPU frequency, offloading weight factor, and energy harvesting is devised. The non-convex nature of the problem necessitates the development of a novel ideal solution group iterative process optimization model that divides the original problem into multiple subproblems, with each subproblem being optimized in turn. According to the results of simulations with a numerical model, our proposed method consumes considerably less energy than just the two benchmark methodologies.

Application of machine learning and statistical approaches for optimization of heavy metals (Cd2+, Pb2+, Cu2+, and Zn2+) adsorption onto carbonized char prepared from PET plastic bottle waste

ABSTRACT This study focuses on the probable use of carbonized char prepared from PET plastic bottles for heavy metals (HMs) adsorption (Cd2+, Pb2+, Cu2+, and Zn2+). The prepared adsorbent is characterized by field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FTIR). Batch adsorption experiments were conducted with the influencing of different operational conditions: contact time (1–180 min), adsorbate concentration (25–300 mg/L), adsorbent dose (0.5–6 g/L), pH (3–7), and temperature (25–60 ºC). High coefficient value [Cd2+ (R2 = 0.99), Pb2+ (R2 = 0.97), Cu2+ (R2 = 0.94), and Zn2+ (R2 = 0.98)] of process optimization model suggest that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 86.68, 73.66, 67.10, and 57.04% for Cd2+, Pb2+, Cu2+, and Zn2+ at pH (7), respectively. Furthermore, ANN and BB-RSM revealed a good association between the tested and projected values. The maximum monolayer adsorption capacity of Cd2+, Pb2+, Cu2+, and Zn2+ was 263.157, 78.740, 196.078, and 84.745 mg/g, respectively. Pseudo-second-order was the well-suited kinetics, where Langmuir and Freundlich isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows HMs adsorption is favorable, exothermic, and spontaneous.

Prediction of bridge structure deformation and strain based on dynamic testing and intelligent algorithms

The field load test is a direct and effective method for evaluating the performance of bridge structures. However, existing bridge field static load tests are costly and inefficient; moreover, they obstruct traffic and cause unavoidable damage to the bridge structure. As an alternative the the static load test, a random model update method based on bridge dynamic load tests and the Bayesian inference is proposed in this paper. The bridge static load test results were predicted with a high accuracy. To speed up the Bayesian method to infer the posterior probability density of the updated parameters, the Gaussian process was used in place of the finite element model, and the Bayesian inference used the Markov chain Monte Carlo method based on the delayed rejection adaptive Metropolis algorithm. First, the parameters to be modified for the bridge structure analysis model were determined based on the global sensitivity analysis method. Second, a uniform design sampling method was used to establish the Gaussian process optimization model to update the random model of the bridge structure. Finally, a reinforced concrete truss arch bridge was used to verify the correctness of the static load results of the bridge predicted by the random model update method based on dynamic load testing and Bayesian inference. The research results reveal that the prediction results of the bridge static load test based on the dynamic load test and Bayesian inference method agree with the actual test results.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Understanding Melt Pool Behavior of 316L Stainless Steel in Laser Powder Bed Fusion Additive Manufacturing.

In the laser powder bed fusion additive manufacturing process, the quality of fabrications is intricately tied to the laser-matter interaction, specifically the formation of the melt pool. This study experimentally examined the intricacies of melt pool characteristics and surface topography across diverse laser powers and speeds via single-track laser scanning on a bare plate and powder bed for 316L stainless steel. The results reveal that the presence of a powder layer amplifies melt pool instability and worsens irregularities due to increased laser absorption and the introduction of uneven mass from the powder. To provide a comprehensive understanding of melt pool dynamics, a high-fidelity computational model encompassing fluid dynamics, heat transfer, vaporization, and solidification was developed. It was validated against the measured melt pool dimensions and morphology, effectively predicting conduction and keyholing modes with irregular surface features. Particularly, the model explained the forming mechanisms of a defective morphology, termed swell-undercut, at high power and speed conditions, detailing the roles of recoil pressure and liquid refilling. As an application, multiple-track simulations replicate the surface features on cubic samples under two distinct process conditions, showcasing the potential of the laser-matter interaction model for process optimization.

Feature analysis of generic AI models for CO2 equilibrium solubility into amines systems

AbstractReported models have disadvantages such as poor prediction accuracy and time‐consuming. And they can not reflect the impact of chemical reactions on CO2 solubility. To compensate for these deficiencies, parameters representing operational parameters, physical properties, chemical properties, and molecular properties are introduced as input variables. A series of models are constructed by three algorithms: back propagation neural network, radial basis function neural network, and random forest. The model with the best prediction performance is level OPCM (RBFNN), with the AARE of only 1.52%. By ranking the importance of the features using the RF algorithm, PCO2, was found to be the key parameter affecting the CO2 loadings, with M being the least important. Using the screened key parameters to model the model, as well as optimizing the structure, can further improve the predictive performance of the model. The full process development and optimization model framework constructed in this article can provide practical guidance for the development of machine learning models.

Autonomous mobile robots for production logistics: a process optimization model modification

Autonomous mobile robots for production logistics: a process optimization model modification

OPTIMIZATION OF STATE REGULATION IN THE FIELD OF SAFETY AND SECURITY OF BUSINESS: A LOCAL APPROACH

The main purpose of the study is to optimize local aspects of state regulation of ensuring the safety and security of enterprises in the context of sustainable development in a particular region. For this purpose, a new methodical approach was presented using simulation optimization of the processes of ensuring safety and security of security of enterprises in the context of sustainable development. The study focuses on enterprises and their safety and security systems security in the context of sustainable development. The relevance of the study is given by the fact that most enterprises in many regions suffer from ineffective regulation at the local level. At the same time, in most cases, state regulation does not take into account all aspects of safety and security in the context of sustainable development. The research methodology involves the use of a modern method of process optimization modeling. As a result, a model for optimizing local aspects of state regulation of business safety and security for a specific, selected region in the context of sustainable development was presented. The research has limitations and they consist of the selection of only one region and the consideration of local aspects of enterprises of certain socio-economic systems. Prospects for further research will be devoted to expanding modeling and taking into account a larger number of enterprises in the context of sustainable development.

The Development and Adaptation of a Predictive Model of Full-Factor Optimisation of the Basic Probiotic Fermented Dairy Product Technological Process

Introduction: In the conditions of rapidly developing markets and the increasing needs of the population for new types of products with long shelf life, there is a need to expand their range, further improve their quality and biological value, and modernize traditional technologies. The main means of achieving these goals is to increase the efficiency and quality of scientific research and accelerate the introduction of scientific achievements in industry. The latter will make adjustments to accelerate the development of effective technologies of whole-milk products. The use of new methods of experiment planning in order to optimize technological processes will reduce the duration of research on the creation of new types of products.Purpose: The article is devoted to the research related to the development and adaptation of a predictive (mathematical) model for optimization of the technological process of probiotic fermented dairy product production. This approach will allow to ensure the design of a certain set of formulations for probiotic fermented dairy product, in which the relative biological value (RBV) will correspond to the maximum value at satisfactory given process parameters.Materials and Methods: The objects of research were: probiotic fermented dairy product manufactured in accordance with the developed mathematical model of the process. The search of specialized literature was carried out using electronic databases: E-library, Scopus, MDPI, Science Direct. Mathematical processing of experimental data for the development of the probiotic fermented dairy product model was carried out using Microsoft Excel 2010 (Microsoft Corporation, Ink.) with the "Solution Search" add-in, as well as specialized software packages TableCurve 2D v.5.01 (SYSTAT Software, Ink.) and Wolfram Mathematica 10.2 (Wolfram Research, Ink.).Results: A mathematical model for optimization of the technological process of probiotic fermented dairy product production was developed. A full-factor experiment (FFE) was conducted, in which the mass fraction of sucrose ( , %) and fat-protein index (r) - the ratio of the mass fraction of fat to the mass fraction of protein in the normalized mixture in the intervals from 0 to 10 and from 0 to 1.5%, respectively, were taken as independent factors. RBV was taken as the resultant factor. Based on the results of the research, a refined mathematical description of the dependence of RBV indicator on the mass fraction of sucrose and fat-protein index was obtained. The set of data on the influence of the mass fraction of sucrose on the dynamics of local maximum of RBV and the dynamics of the corresponding values of the mass fraction of fat showed that the maximum of RBV equal to 244.866% corresponded to the mass fraction of sucrose equal to 7.31%. The latter corresponded to the optimum fat-protein index of 1.241. In order to ensure maximum RBV, the lower limit of the mass fraction of fat had to correspond to 3.475%. The analysis of the FFE data showed the presence of additional corrective factors of RBV indicator dependence on fat and protein mass fraction indices. To refine the developed model taking into account additional factors, a correction factor (Q) was introduced into it.Conclusion: Approbation of the optimization model of the technological process of probiotic fermented dairy product production and the obtained mathematical dependencies can serve as a criterion for the formation of a set of formulations for the production of sugar-containing fermented dairy products with a high potential of relative biological value. It should be taken into account that the definition area of this approach is limited in terms of mass fraction of fat in the range from 0.06 to 4.84% and mass fraction of protein - from 2.8 to 3.9%. With this approach (model development) it is possible to analyze promising modes for the studied process, which in real conditions at the experimental stage are not always possible to obtain. The development of this model will significantly reduce the duration of research on the optimization of the technological process.

Optimization of Food Processing Parameters Based on Parametric Models

Abstract In this paper, a food processing process optimization model is constructed based on the improved particle swarm algorithm. By adding the chaos mapping strategy to the basic particle swarm algorithm, the optimization efficiency of the particle swarm under the constraints is improved. Combining the multi-objective optimization capability of the NDWPSO algorithm, the process parameter optimization process is constructed to optimize the parameters of food processing processes. The proposed method for process parameter optimization is applied to the vacuum drying process parameter optimization experiment of Jujube to test the effectiveness of particle swarm process parameter optimization. The results show that the diversity value and the best-adapted value of the NDWPSO algorithm in the single-peak function only use a small number of iterations to drop to a level close to 0, indicating that the NDWPSO algorithm has a faster convergence speed. In the vacuum drying process of jujube, the theoretical values of the optimal process parameters for freeze-dried jujube slices are: jujube slices thickness 5mm, sublimation drying temperature -22℃, resolution drying temperature 20℃.

Multi-objective optimization of key process parameters in laser cladding Stellite12 cobalt-based alloy powder

Laser cladding (LC) process parameters have a substantial influence on coating morphology and mechanical characteristics; it is necessary to optimize key parameters for laser processing. In this study, Stellite12 cobalt-based alloy powder with excellent corrosion and wear resistance was selected as the cladding material. The multi-objective optimization model of the LC process was established by response surface methodology, laser power, scanning speed, and powder feeding rate as input factors, and the target response variables involve dilution, aspect ratio, and microhardness of the single-track cladding. Combined with variance analysis (ANOVA), the multi-objective optimization of laser power, scanning speed, and powder feeding rate was conducted. A single-track cladding layer with a dilution of 18.29%, an aspect ratio of 3.88, and a microhardness of 634.67 HV0.2 was obtained using the optimized process parameters. Errors between the predicted and actual values of single-track cladding dilution, aspect ratio, and microhardness were less than 8%, which verified the accuracy of the established model.