Optimization Model Of Process Research Articles

Digital Transformation Strategy of Macys: Addressing Challenges and Achieving Revival

Abstract: This study explores the challenges faced by Macys in the United States and its strategy for digital transformation. Firstly, it introduces the rise and fall of the U.S. department store industry and the challenges Macys faces as a representative of the traditional department store industry, such as the rise of e-commerce and changes in consumer shopping habits. Then, taking Macys as a case study, it describes its digital transformation strategy, Polaris, including cost reduction, accelerated digitization, reshaping the supply chain and other initiatives and the results achieved. It then proposes specific plans for staffing adjustments, enterprise transaction process optimization and online business model modifications. Finally, the challenges that may be faced in implementing digital transformation programs, such as data silos, change management and financial overheads, are analyzed, and solutions are proposed. By summarizing and learning from the successful experiences of modern enterprises, this study proposes a series of feasible digital transformation plans for Macy's to emerge from the predicament of the traditional department store industry, meet the challenges of the digital era, and achieve a renaissance.

Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design

Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However, one crucial limitation of existing methods is the inability to tackle social bias while maintaining satisfactory model performance. In this paper, we aim to fill this gap by proposing a sentiment classification method that entails ethnicity-aware algorithmic design. Specifically, our method involves balanced training and a custom ethnicity-aware loss function that leverages ethnicity group information to foster a fair model optimization process. The proposed loss incentivizes the model to iteratively improve accuracy for currently underperforming demographic or social groups, therefore simultaneously decreasing social bias and boosting overall performance. Our extensive qualitative and quantitative experimental evaluation involving a large corpus of user reviews demonstrated the effectiveness of the proposed method, also when compared to popular baselines for sentiment classification.

Integrating different fidelity models for process optimization: A case of equilibrium and rate-based extractive distillation using ionic liquids

We integrate equilibrium and rate-based models to formulate a hybrid optimization scheme for designing an ionic liquid-based extractive distillation process for mixed-refrigerant separation. The equilibrium model assumes vapor–liquid equilibrium at each stage but challenges arise with low-volatility, high-viscosity solvents, which drive the system away from equilibrium. The rate-based approach considers mass and heat transfer rates, giving more accurate representation. We compare the two models for separating R-410A, an azeotropic mixture of R-32 and R-125, using [EMIM][SCN] ionic liquid as entrainer. Analyzing over 4300 simulations with various dimensionality reduction and topological analysis techniques, we find that predictions from the two models exhibit similar trends, but the overestimation in equilibrium-based purities sometimes leads to infeasible process designs. The proposed optimization algorithm thus combines the strengths of the two models to locate feasible and optimal designs.

The Evolution of Dilatant Shear Bands in High-Pressure Die Casting for Al-Si Alloys

Bands of interdendritic porosity and positive macrosegregation are commonly observed in pressure die castings, with previous studies demonstrating their close relation to dilatant shear bands in granular materials. Despite recent technological developments, the micromechanism governing dilatancy in the high-pressure die casting (HPDC) process for alloys between liquid and solid temperature regions is still not fully understood. To investigate the influence of fluid flow and the size of externally solidified crystals (ESCs) on the evolution of dilatant shear bands in HPDC, various filling velocities were trialled to produce HPDC samples of Al8SiMnMg alloys. This study demonstrates that crystal fragmentation is accompanied by a decrease in dilatational concentration, producing an indistinct shear band. Once crystal fragmentation stagnates, the enhanced deformation rate associated with a further increase in filling velocity (from 2.2 ms−1 to 4.6 ms−1) localizes dilatancy into a highly concentrated shear band. The optimal piston velocity is 3.6 ms−1, under which the average ESC size reaches the minimum, and the average yield stress and overall product of strength and elongation reach the maximum values of 144.6 MPa and 3.664 GPa%, respectively. By adopting the concept of force chain buckling in granular media, the evolution of dilatant shear bands in equiaxed solidifying alloys can be adequately explained based on further verification with DEM-type modeling in OpenFOAM. Three mechanisms for ESC-enhanced dilation are presented, elucidating previous reports relating the presence of ESCs to the subsequent shear band characteristics. By applying the physics of granular materials to equiaxed solidifying alloys, unique opportunities are presented for process optimization and microstructural modeling in HPDC.

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

Supercritical CO2 as a green technology for carotenoids-rich virgin palm oil production: Process optimization, kinetics and thermodynamics modeling

The experimental conditions of supercritical carbon dioxide (scCO2) extraction of virgin palm oil (VPO) from oil palm mesocarp fiber (OPMF) were optimized using the Response Surface Methodology (RSM). Results showed that a maximum of 28.68% of VPO extraction was obtained at the optimal experimental conditions of scCO2 extraction: pressure of 31MPa, temperature of 340K, and extraction time of 80min. The second-order rate equation, Arrhenius equation and Eyring theory were employed to assess the kinetics behaviour, activation energy and thermodynamics behaviour of scCO2 extraction of VPO from oil palm mesocarp fiber. The lower activation energy value (12.17kJ/mol) of scCO2 for the extraction of VPO from OPMF indicates that the scCO2 extraction technology is less dependent on temperature during the extraction of VPO from OPMF. The physicochemical properties and fatty acids compositions analyses reveal that the scCO2 extracted VPO contains high carotenoids content (982μg/g), low free fatty acids content (0.31wt.%), higher oxidative stability and higher unsaturated fatty acids content.

Edge-assisted U-Shaped Split Federated Learning with privacy-preserving for Internet of Things

In the realm of the Internet of Things (IoT), deploying deep learning models to process data generated or collected by IoT devices is a critical challenge. However, direct data transmission can cause network congestion and inefficient execution, given that IoT devices typically lack computation and communication capabilities. Centralized data processing in data centers is also no longer feasible due to concerns over data privacy and security. To address these challenges, we present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers to assist IoT devices in model training and optimization process. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data, thereby enhancing data privacy protection by transmitting only model parameters. Additionally, inspired by Split Learning (SL), we split the neural network into three parts using U-shaped splitting for local training on IoT devices. By exploiting the greater computation capability of edge servers, our framework effectively reduces overall training time and allows IoT devices with varying capabilities to perform training tasks efficiently. Furthermore, we proposed a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks, eliminating the risk of privacy leakage. Our theoretical analysis and experimental results demonstrate that EUSFL can be integrated with various aggregation algorithms, maintaining good performance across different computing capabilities of IoT devices, and significantly reducing training time and local computation overhead.

Health state assessment model for complex systems: Trade-off accuracy and robustness in belief rule base

In complex system, health state assessment can determine the state of the system and identify potential system problems. However, due to the numerous uncertainties and variations present in complex systems, it is difficult to effectively construct assessment models. Belief rule base (BRB) can use data-driven and knowledge-driven methods to effectively address uncertain information, and is widely used for modeling health state assessments of complex systems. The primary modeling and optimization goals of BRB is currently at accuracy, ignoring the impact of robustness on complex systems, and the reliability of the model is reduced. Therefore, this article introduces a novel method to balance the accuracy and robustness of BRB models. This method enhances the performance of the BRB model in assessing complex system health and provides valuable guidance for engineering applications. Firstly, the guidelines for BRB modeling are systematically summarized to address the trade-off between accuracy and robustness. This provides essential guidance for constructing BRB models during the model-building process. Secondly, four feasible domain criteria are proposed to enhance the reliability of the BRB during the model optimization process. A modified multi-objective optimization algorithm is proposed based on the feasible domain criteria. Finally, in the case studies of aerospace relay and lithium-ion battery health assessments, the MSE of the proposed model for aerospace relay health assessment is 0.0015 with a Lipschitz constant of 6.73, while for lithium-ion battery health assessment, the MSE is 0.0013 with a Lipschitz constant of 24.17. The experimental results demonstrate that the proposed model has an advantage in terms of the trade-offs between both robustness and accuracy.

Optimized energy efficient reactive distillation for octane upgrading with economic and environmental considerations

Advancing fuel efficiency and production technologies can significantly reduce “Well to Wheels” and “Octane on Demand” emissions. This has been accomplished by integrating intensified technologies in fuel refineries. However, maintaining low energy consumption and high octane number while considering the impact of operating conditions under uncertainty on the process feasibility remains a challenge. Therefore, this article proposes a novel absorption heat pump reactive distillation (AHPRD). Aiming to maximize the economic potential of the AHPRD, an optimization framework that considers operating conditions under uncertainty (i.e. temperature (T), pressure (P), reflux ratio (RR), and feed stage (FS)) is developed. The optimization framework synchronizes Monte Carlo simulation and particle swarm optimization (PSO) to maximize the net present value (NPV). The framework used a hybrid Python-DWSim interface platform. The AHPRD was further investigated and compared against existing processes, namely, a conventional process refinery (CP) and reactive distillation (RD), to improve energy saving, increase financial return, and cut CO2 emissions. Attractively, the AHPRD outperforms existing processes, both economically and environmentally, doubling profit values and avoiding 52.2% − 77.4% of CO2 emissions. This study believes that the future direction will be to build an artificial intelligence model for process optimization and economic prediction.

Building information modeling-based production process optimization model

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency.

Surrogate gradient methods for data-driven foundry energy consumption optimization

In many industrial applications, data-driven models are more and more commonly employed as an alternative to classical analytical descriptions or simulations. In particular, such models are often used to predict the outcome of an industrial process with respect to specific quality characteristics from both observed process parameters and control variables. A major step in proceeding from purely predictive to prescriptive analytics, i.e., towards leveraging data-driven models for process optimization, consists of, for given process parameters, determining control variable values such that the output quality improves according to the process model. This task naturally leads to a constrained optimization problem for data-driven prediction algorithms. In many cases, however, the best available models suffer from a lack of regularity: methods such as gradient boosting or random forests are generally non-differentiable and might even exhibit discontinuities. The optimization of these models would therefore require the use of derivative-free techniques. Here, we discuss the use of alternative, independently trained differentiable machine learning models as a surrogate during the optimization procedure. While these alternatives are generally less accurate representations of the actual process, the possibility of employing derivative-based optimization methods provides major advantages in terms of computational performance. Using classical benchmarks as well as a real-world dataset obtained from an industrial environment, we demonstrate that these advantages can outweigh the additional model error, especially in real-time applications.

Process optimization and kinetic modeling of ultrasound assisted lactic acid production

AbstractThe impact of ultrasound‐assisted fermentation using Lactobacillus species was investigated, for enhanced lactic acid production. The different ultrasound frequencies of 20, 30, and 40 Hz, with time intervals of 3, 4, and 5 min, and with corn steep liquor concentrations of 1%–3% were examined using response surface methodology‐Box–Behnken design. The growth of lactic acid production was modeled by Contois model (linear) and Baranyi–Roberts model (nonlinear), assessing the suitability of Lactobacillus paracasei, Lactobacillus plantarum, and Lactobacillus acidophilus for industrial processes. At 34 h, the maximal lactic acid generation of L. paracasei, L. plantarum, and L. acidophilus was 85.2%, 82.1%, and 79.4%, respectively. The R2, μmax, and Ks values for L. paracasei and L. plantarum were found to be 0.9158, 0.574 h−1, 54.27 g/L and 0.9426, 0.294 h−1, 22.66 g/L, respectively, using Contois model. The observed R2 values of Baranyi–Roberts model for the strains L. acidophilus, L. paracasei, and L. plantarum were 0.9947, 0.9954, and 0.9998, respectively.Practical ApplicationsLactic acid is crucial in the fermentation process for products like yogurt, sauerkraut, kimchi, and pickles, enhancing flavor, texture, and shelf life. As a precursor to biodegradable plastics, lactic acid contributes to the development of sustainable packaging materials and environmentally friendly alternatives to traditional plastics. Utilizing waste corn steep liquor (CSL) as a growth medium presents a sustainable and cost‐effective solution for lactic acid production, capitalizing on its rich nutrient profile to enhance microbial growth. As a byproduct of corn wet milling, using CSL for fermentation processes promotes waste valorization and contributes to a circular economy. The optimization of fermentation conditions using the Box–Behnken design ensures maximum yield and efficiency by systematically evaluating the effects of key process variables. Integrating advanced growth curve models like the Contois and Baranyi models further refines the understanding and prediction of microbial dynamics, enabling precise control over the fermentation process. This comprehensive approach not only promotes the efficient use of industrial by‐products but also advances the development of environmentally friendly and economically viable bioprocesses.

Continuous-flow synthesis of di-(2-ethylhexyl) peroxydicarbonate in a packed-bed microreactor: Process optimization and kinetic modeling

Di-(2-ethylhexyl) peroxydicarbonate (EHP) is a low-temperature liquid organic peroxide commonly used as an initiator for polymerization of vinyl chloride and ethylene. The exothermic nature of peroxidation and the instability of this peroxide product pose safety challenges during synthesis. Therefore, batch reaction processes for synthesizing such products often prioritize safety over efficiency. In this work, an efficient and safe protocol was proposed for EHP production with the use of a packed-bed microreactor. Response surface methodology was applied to optimize critical process parameters, resulting in a remarkable yield of 95.48% in a residence time of 23.8 min. It proves that the efficiency was significantly improved by the packed-bed microreactor compared with batch reactors. Additionally, an operational window was established to guide the process development for synthesizing EHP in flow. Finally, a kinetic model was established, which was of crucial theoretical significance and practical value for better understanding peroxidation processes and parameter optimization.

Analysis of the privacy-performance tradeoff of reference testing in Forensic Investigative Genetic Genealogy.

During an investigation using Forensic Investigative Genetic Genealogy, which is a novel approach for solving violent crimes and identifying human remains, reference testing-when law enforcement requests a DNA sample from a person in a partially constructed family tree-is sometimes used when an investigation has stalled. Because the people considered for a reference test have not opted in to allow law enforcement to use their DNA profile in this way, reference testing is viewed by many as an invasion of privacy and by some as unethical. We generalize an existing mathematical optimization model of the genealogy process by incorporating the option of reference testing. Using simulated versions of 17 DNA Doe Project cases, we find that reference testing can solve cases more quickly (although many reference tests are required to substantially hasten the investigative process), but only rarely (<1%) solves cases that cannot otherwise be solved. Through a mixture of mathematical and computational analysis, we find that the most desirable people to test are at the bottom of a path descending from an ancestral couple that is most likely to be related to the target. We also characterize the rare cases where reference testing is necessary for solving the case: when there is only one descending path from an ancestral couple, which precludes the possibility of identifying an intersection (e.g., marriage) between two descendants of two different ancestral couples.

A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model's optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

AbstractThis study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

Online optimization of petrochemical process via case-based reasoning and conditional mutual information

The strict mechanism model of petrochemical processes is often complex, resulting in long modeling times, low computational efficiency, and poor accuracy, which limits the application of mechanistic models in process optimization and advanced control. The advent of big data technology provides new solutions for this problem. Herein, a new method based on historical Case-Based Reasoning (CBR) is proposed to process online optimization and calculate variable attribute weights for case retrieval using conditional mutual information. Considering the process time-lag characteristics, a piecewise sequential CBR method for optimization is further proposed, which directly recognizes the pattern based on the industrial past data and amply utilized the process information. For case study and method validation, the approach is applied to an actual fluid catalytic cracking (FCC) process, converting low-quality heavy oils into valuable light transportation fuels and chemicals such as gasoline and propylene. As demonstrated by the case study results, the proposed method increases the average yield of gasoline and total liquid respectively by 2.5 % and 4.0 % while decreasing the average yield of coke by 1.3 %. The results further indicate that the overall optimization performance is comparable to many advanced intellectual optimization algorithms, allowing favorable operability and ensuring good robustness for different optimization targets. It could provide a solution with high accuracy and good adaptability for online process optimization in complex chemical processes.

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