Objective Function Research Articles

Engineered autologous nasal cartilage for repair of nasal septal perforations – a case series

Objective: This phase I clinical trial assessed the use of autologous nasal chondrocyte tissue-engineered cartilage (N-TEC) for functional repair of nasal septal perforations (NSP). Background: The most widely used technique to treat NSP, namely interposition grafting with a polydioxanone (PDS) plate combined with a deep temporal fascia (DTF) graft, is still suboptimal towards patient satisfaction and revision rates. Methods: Patients (n=5, all female, age range: 23-54 years) had a 0.5-2.0 cm diameter NSP. N-TEC was manufactured by expansion and 3D culture of autologous nasal septum chondrocytes into Chondro-Gide® collagen membranes. N-TEC was then shaped intraoperatively and enveloped in the harvested DTF before suturing it into the NSP. Safety (primary outcome) was assessed by the number of serious adverse reactions (SAR) until 12 months. Secondary outcomes included feasibility, assessed by surgical graft manipulation, and efficacy, assessed using subjective scoring (Nasal Obstruction Symptom Evaluation, NOSE, and Visual Analogue Scale, VAS, scores) and objective breathing function tests. Structural closure of NSP after 12 months was defined using endoscopy and computed tomography (CT) scans. Results: NSP treatment by N-TEC implantation was safe and feasible, as no SAR and no challenge in graft manipulation was recorded for any of the patients. One year postoperative, subjective scoring improved in all patients, unless already optimal (average improvement of 23 and 28.6 points out of 100 respectively for NOSE and VAS scores). Objective respiratory function overall confirmed – with the exception of one case – the observations above (average improvement of 172 ml/s). NSP were closed and the mucosae completely healed in three patients. Conclusion: Autologous N-TEC is a valid treatment for NSP and warrants further clinical tests.

Comparative effectiveness of interventions for cancer treatment–related cognitive impairment in adult cancer survivors: protocol for a systematic review

BackgroundCancer treatment–related cognitive impairment (CTRCI) can substantially reduce the quality of life of cancer survivors. Many treatments of CTRCI have been evaluated in randomized controlled trials (RCTs), including psychological interventions, pharmacologic interventions, and other therapies. There is a pressing need to establish the benefits and harms of previously studied CTRCI treatments. The proposed systematic review and network meta-analyses will assess the relative efficacy and safety of competing interventions for the management of CTRCI.MethodsIn consultation with the review team, an experienced medical information specialist will draft electronic search strategies for MEDLINE®, Embase, CINAHL, PsycINFO, and the Cochrane Trials Registry. We will seek RCTs of interventions for the treatment of CTRCI in adults with any cancer, except cancers/metastases of the central nervous system. Due to the anticipated high search yields, dual independent screening of citations will be expedited by use of an artificial intelligence/machine learning tool. The co-primary outcomes of interest will be subjective and objective cognitive function. Secondary outcomes of interest will include measures of quality of life, mental and physical health symptoms, adherence to treatment, and harms (overall and treatment-related harms and harms associated with study withdrawal), where feasible, random-effects meta-analyses and network meta-analyses will be pursued. We will address the anticipated high clinical and methodological heterogeneity through meta-regressions, subgroup analyses, and/or sensitivity analyses.DiscussionThe proposed systematic review will deliver a robust comparative evaluation of the efficacy and safety of existing therapies for the management of CTRCI. These findings will inform clinical decisions, identify evidence gaps, and identify promising therapies for future evaluation in RCTs.

An Improved CH4 Profile Retrieving Method for Ground-Based Differential Absorption Lidar

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites.

Soft computing models for prediction of bentonite plastic concrete strength

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Investigation of home appliances industry and devices obsolescence considering energy consumption

The objective of this study is to optimize a linear objective function that pertains to the obsolescence of home appliances in a household. The approach introduced is an ILP-based method that is both simple and adaptable. This optimization guarantees that the total energy consumption remains below a predetermined energy threshold, while also adhering to an inequality constraint. The proposed methodology was effectively tested using a household containing four home appliances: a washing machine, a dishwasher, a refrigerator, and an oven. The findings indicate that the refrigerator is the most hazardous and inexpensive home appliance. The current formulation aims to optimize the device's lifespan considering the customer's financial constraints and the expenses associated with repairing and replacing its parts. This study centers on the maintenance of printed circuit boards (PCBs) and considers the effects of degradation-related malfunctions, as assessed through testing. Moreover, we employ an additional ILP approach to assess the vulnerability of refrigerator’s PCB components by determining which ones should be replaced when a customer has limited financial resources.

Advanced control and energy management algorithm for a multi-source microgrid incorporating renewable energy and electric vehicle integration

This paper presents a comprehensive study on advanced control and energy management in a multi-source/multi-load microgrid. The microgrid integrates solar panels, a wind turbine system, a Li-ion battery-based storage system, an electric vehicle (EV), and a DC load, all inter-connected through power electronic converters to a DC bus and linked to the AC grid via a DC/AC inverter. The study outlines the primary control objectives: DC bus voltage regulation, optimization of photovoltaic and wind energy conversion, and maintaining high-quality energy injection into the grid. Given the intermittent nature of solar and wind energy and the varying energy demands that affect battery life and performance, a novel energy management algorithm is introduced. This algorithm addresses different operating modes, such as Constant Current (CC) and Constant Voltage (CV), to enhance network stability, energy quality, and optimize energy distribution between the various sources and the EV. The proposed fuzzy logic controllers, known for their robustness to uncertainties, flexibility, and ease of implementation, are employed to achieve these objectives. The Particle Swarm Optimization (PSO) is used to fine-tune the fuzzy logic control parameters, ensuring optimal performance. This optimization process included minimizing Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Square Error (ITSE) as objective functions. Extensive simulations under various operating scenarios demonstrated significant improvements in control accuracy, stability, and system performance.

Full quantum eigensolvers based on variance

The advancement of quantum computation paves a novel way for addressing the issue of eigenstates. In this paper, two full quantum eigenvalue solvers based on quantum gradient descent are put forward. Compared to the existing classical-quantum hybrid approaches such as the variance-variational quantum eigenvalue solver, our method enables faster convergent computations on quantum computers without the participation of classical algorithms. As any eigenstate of a Hamiltonian has zero variance, this paper takes the variance as the objective function and utilizes the quantum gradient descent method to optimize it, demonstrating the optimization of the objective function on the quantum simulator. With the swift progress of quantum computing hardware, the two variance full quantum eigensolvers proposed in this paper are anticipated to be implemented on quantum computers, thereby offering an efficient and potent calculation approach for solving eigenstate problems. Employing this algorithm, we showcase 2 qubits of deuterium and hydrogen molecule. Furthermore, we numerically investigate the energy and variance of the Ising model in larger systems, including 3, 4, 5, 6, and 10 qubits.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

Consensus local graph for multiple kernel clustering

Due to the ability to represent the relationship among data, graph has received extensive attention in clustering field. As illustrated by existing studies, learning graph in kernel space is an effective way to capture the structure of data. Among kernel-based methods, multiple kernel learning (MKL) is increasingly popular since it can automatically utilize the complementary information contained in base kernels. Although many existing MKL-based graph learning algorithms have promising learning abilities, we observe that they (1) put too much attention on learning the consensus kernel which probably contains lots of redundant information and loses the diversity of base kernels; (2) ignore the local structure of the representations in multiple kernel spaces. To eliminate the bad effect of these issues, we propose a novel graph learning method, termed consensus local graph based on MKL (CLGMKL), for clustering. In CLGMKL, a low-rank kernel matrix is used to extract the discriminative information of each base kernel and a local graph is constructed to capture the local structure contained in each new kernel. Then CLGMKL combines these local graphs in a self-weighed way. Since the above processes are associated with each other, we jointly optimize them to obtain the overall optimality. An effective learning scheme with proved convergence is developed to optimize the combined objective function. Finally, extensive experiments on some popular datasets are conducted to test the effectiveness of the presented method. As illustrated, CLGMKL is more competitive than the state-of-the-art algorithms.

Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy

In model predictive control (MPC), a process dynamic model is utilized to make predictions of the value of the objective function and constraints throughout a prediction horizon. In one method of solving this problem, the time required to find the optimal values of the decision variables depends on the time required to perform the arithmetic operations involved in computing the model predictions. Methods for attempting to reduce the computation time of an MPC could then include developing approximate (reduced-order or data-driven) models for a system that take less time to solve, or to parallelize the computations using, for example, multiple cores or CPU’s. However, an observation in all of these cases is that the values of the process states across the prediction horizon are not the values returned by the optimization problem; the manipulated input trajectory is the desired decision variable. An optimization strategy that cannot explicitly return the process states but can generate some representation of them that then leads to computation of the desired process input would thus be suitable for MPC. Quantum computers achieve their parallelism through creating values that cannot all be returned. They can then operate on this set of values to return a number that is meaningful with respect to that set of values that could not all be returned. Motivated by this, we wish to investigate an idea for utilizing quantum parallelism in developing a representation of an objective function that depends on the solution of a process dynamic model, but then only returning the control actions that minimize the objective function value dependent on those solutions. To do this, several steps are necessary. The first is to locate a quantum algorithm which has the desired characteristics for achieving the goals. In this work, we perform these steps using an amplitude amplification strategy based on Grover’s algorithm on a quantum computer. The second is to analyze the algorithm with respect to its ability to translate across problems in the MPC domain, with respect to both its ability to handle nonlinear systems and to handle a variety of different structures of the set of all possible objective function values given the allowable values of the decision variables. We thus evaluate the benefits and limitations of the algorithm from this perspective. We do not wish to imply that this algorithm is more computationally-tractable for use with MPC than classical optimization techniques traditionally applied in an MPC context. Rather, we wish to understand the manner in which such an algorithm would be designed and when it is appropriate for MPC problems (in the sense of returning the correct answers to the optimization problem), as a step toward better understanding the interactions of the quantum properties of quantum algorithms with control goals and forming an important first step in algorithm design/analysis, which can then translate to future works in comparing this technique computationally with classical “competitor” algorithms to guide further work in searching for relevant quantum algorithms for control applications.

Multi-objective Reactive Power Optimization of a Distribution Network based on Improved Quantum-behaved Particle Swarm Optimization

Background: Reactive power optimization (RPO) is crucial for distribution networks in the context of large-scale renewable distributed generation (RDG) access. Objective: To address the problems caused by the connection of RDG, an RPO model and an improved quantum-behaved particle swarm optimization (IQPSO) algorithm are proposed. Method: In this study, a dynamic S-type function is proposed as the objective function of the minimum active power loss, whereas an exponential function is proposed as the objective function of the minimum voltage deviation to establish an RPO objective function. The operating cost of distribution is considered as the third objective function. To address the RPO problem, a QPSO algorithm based on the ε-greedy strategy is proposed in this paper. ModifiedIEEE33 bus and IEEE69 bus systems were used to evaluate the proposed RPO method in simulations. Results: The simulation results reveal that the IQPSO algorithm obtains a better solution, and the proposed RPO model can considerably reduce active power loss, node voltage deviation, and distribution network operating costs. Conclusion: The RPO model and IQPSO algorithm proposed in this study provide a highperformance method to analyze and optimize reactive power management in distribution network.

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

Improving mechanical properties of lattice structures using nonuniform hollow struts

In contrast to constant-strut lattices, the introduction of innovative strut designs has the potential to enhance the mechanical properties of lattice structures. This study presents a Bézier curve-based nonuniform section design for body-centered cubic lattices with hollow struts (BCCH). Periodic boundary conditions are applied to the unit cells, and a finite element (FE) numerical homogenization method is employed to assess their elastic properties. A comprehensive dataset is generated through the FE model, which is subsequently divided into training, validation, and testing sets. The coordinates of the Bézier curve control points serve as inputs to deep learning networks, which are trained on the training dataset. Relative density and elastic properties are treated as two distinct networks, with the validation set utilized to prevent overfitting. The objective function consists of two weighted components: one aims to maximize the relative Young's modulus, while the other ensures that the relative density achieves a specified value. An evolutionary algorithm is employed to optimize the objective function, with variations in the control point coordinates constrained to specific ranges. By leveraging the fast inference ability of the deep learning model, the stiffness and orientation-dependent mechanical properties can be efficiently tailored. Our results demonstrate that the optimized structures demonstrate superior stiffness (+92.8%) and distributed stress field compared to the benchmark lattice. The design method also enables tailoring of specific mechanical properties, including isotropic elasticity. 3D-printed lattice designs were fabricated and compression tests confirmed agreement with simulation results in terms of stiffness. Additionally, the optimized designs exhibit superior strength (+99.6%) and toughness compared to the benchmark lattices.

Topology optimization with vortex-intensity-based objective function enables novel film cooling hole with superior performance

Film cooling is widely adopted in the thermal protection of modern aerospace engines. The optimization of the film hole is an important issue in thermal designs. In this work, a density-based topology optimization is conducted to obtain a high-performance film cooling hole geometry. Unlike the traditional wall-temperature-based optimization objectives, a novel objective function based on the Q-criteria is proposed by taking the vorticity dynamics and flow mixing into account. Finally, a unique high-performance film hole is obtained, which shows superior performance compared to the standard cylindrical hole, particularly at high blowing ratios. The film cooling effectiveness can be elevated by 51 folds in the case of a high blowing ratio (M = 1.5). Analyses of the vortices and velocity fields suggest that the presence of the additional secondary vortex pair enhances the attachment of cooling air to the wall.

Nearby allocation of emergency repair resources for multiple faults in distribution networks based on parallel CNN algorithm

In order to improve the efficiency of emergency repair for multiple faults in distribution networks, a method for allocating emergency repair resources for multiple faults in distribution networks based on parallel convolutional neural network (CNN) algorithm is studied. This method uses a matrix operation based multiple fault detection method for distribution networks. After determining the location of multiple faults based on the direction of fault power, the principle is to allocate emergency repair resources nearby for multiple faults, with the goal of minimizing economic losses caused by the fault point and minimizing fault repair time. The objective function for allocating emergency repair resources nearby is constructed, and parallel CNN algorithm is used to solve the problem by classification, to find the feasible solution with the minimum mean square error between the objective function and the feasible solution set for nearby allocation of emergency repair resources, and it is used as the optimal solution for nearby allocation of repair resources. The experimental results show that when the proposed method is used to allocate emergency repair resources for multiple faults in the distribution network, the optimal time for setting the repair plan is 0.53 and 0.96 s, respectively, with an average allocation accuracy of 91%. It has been confirmed that this method can achieve the optimal decision of resource allocation plan in a short period of time, improving the efficiency of emergency repair.

4E analysis and multi-objective optimization of a novel multi-generating cycle based on waste heat recovery from solid oxide fuel cell fed by biomass

The present study optimizes a novel developed cycle including solid oxide fuel cell (SOFC) fed by synthesis gas produced from biomass as well as gas turbine (GT), supercritical carbon dioxide cycle (SCO2), transcritical carbon dioxide cycle (TCO2), Organic Rankine Cycle (ORC), thermoelectric generator (TEG), and reverse osmosis (RO)- based desalination. Energy, exergy, exergoeconomic and exergoenvironmental analyses on the developed cycle were investigated. Multi-objective optimization was carried out using of Genetic algorithm using generated power and exergy destruction as objective functions. Sankey diagram data indicate that afterburner holds the highest portion of the total exergy destruction 46.5% (692.24 kW), followed by SOFC which is 20.48% (304.51 kW). Moreover, optimization results showed that the total net power, first and second laws of thermodynamic efficiencies increased by 2.6%, 0.96% and 0.83%, respectively, while exergy destruction decreased by 1%. Furthermore, such a power increase (18.53 kW) using the freshwater produced by RO leads to daily production of 17040 liters of drinking water. According to the exergoeconomic analysis, the minimum flow value pertains to GT at a value of 0.0119 $/GJ, while the TCO2 turbine has the highest value which is 0.2867 $/GJ. The system product cost rate and exergy destruction cost rate reached 27.0353 $/h, and 10.7012 $/h, respectively. In the case of the exergoenvironmental one, the maximum environmental impact is related to the SCO2 turbine 0.0212 Pts/GJ, while SOFC has the lowest (0.0002 Pts/GJ). The system product environmental impact and exergy destruction were achieved at optimum values of 2.7503 $/h, and 4.1576 ×10-7 $/h, respectively.

Exergoeconomic and environmental optimization of gas turbine-based CCHP systems: A comprehensive study with multi-objective optimization and decision making

This study comprehensively assesses two gas turbine-based combined cooling, heating, and power (CCHP) systems, employing exergoeconomic and environmental evaluations. Utilising a dedicated in-house code within the MATLAB environment, the study conducts a thorough performance analysis of the CCHP systems under base case operating conditions. The base case results demonstrate the superiority of the first system over the second system in various aspects. Exergy analysis indicates the combustion chamber as the key source of irreversibility, accounting for more than 70 % of exergy destruction in both systems, emphasising the need to address combustion-related inefficiencies. Furthermore, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to perform multi-objective optimisation of both systems, taking system cost rate and normalized CO2 emissions as objective functions. The investigation encompasses ten optimisation runs for each system, followed by statistical analysis to ensure methodological reliability. The multi-criteria decision analysis is also performed to select the final optimal solution from the Pareto fronts by using a novel approach of integrating Entropy method with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-maker. The study showcases the significant improvements in system performance and environmental impact achieved through multi-objective optimization. Both systems experience around 8–9 % reductions in system cost rates and around 1.5–2 % reduction in the normalized CO2 emissions. Scattered distribution plots highlight the trade-offs amongst key decision variables, particularly the pressure ratio and isentropic efficiency of the air compressor. In conclusion, this research offers valuable insights into the performance, efficiency, and optimization potential of CCHP systems.

Enhancing thermodynamic performance with an advanced combined power and refrigeration cycle with dual LNG cold energy utilization

Utilizing waste heat to drive thermodynamic systems is imperative for improving energy efficiency, thereby improving sustainability. A combined cooling and power systems (CCP) utilizes heat from a temperature source to deliver both power and cooling. However, CCP systems utilizing LNG cold energy suffers from low second law efficiency due to significant temperature differences. To address this, an “Advanced Power and Cooling with LNG Utilization (ACPLU)” system is proposed, integrating a cascaded transcritical carbon dioxide (TCO2)-LNG cycle with an Organic Rankine cycle (ORC) for improved power generation and an absorption refrigeration system (ARS) for simultaneous cooling. This study evaluates the second law efficiency, net work output, and exergy destruction performance through a sensitivity analysis, optimizing variables such as heat source temperature, superheater temperature difference, ORC and CO2 turbine inlet and condenser pressures, evaporator temperature, and pinch point temperatures of heat exchangers and generator. Compared to previous studies on CCP systems, the ACPLU shows a superior performance, with a second law efficiency of 27.3 % and a net work output of 11.76 MW. Cyclopentane as an ORC working fluid resulted in the highest second law efficiency of 29.06 % and net work output of 12.27 MW. Parametric analysis suggested that heat source temperature significantly impacts net power output. The exergy analysis concluded that a high-pressure ratio and good thermal match between the heat exchangers enhance overall performance. Utilizing artificial neural network (ANN) to produce a multiple-input-multiple-output (MIMO) objective function and performing multi-objective optimization (MOO) using genetic algorithm (GA), an improved second law efficiency and net power output by 28.11 % and 14.16 MW respectively, with pentane as the working fluid, is demonstrated. An average cost rate of 9.121 $/GJ was observed through a thermo-economic analysis. The ACPLU system is promising for medium temperature waste heat recovery, such as, pharmaceutical manufacturing plants.

Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network

This study aims to establish a novel framework for mixture design optimization of engineered cementitious composite (ECC) by first collecting two original datasets of ECC’s tensile stress and strain from the extensive and credible literature. The datasets comprise a wide range of variables including cementitious ingredients, 9 types of fiber and characteristics, admixtures, and experimental conditions. The data augmentation is then performed using a tuned constraints-modified Conditional Tabular Generative Adversarial Network (Tuned-CTGAN) to increase the model accuracy and generalizability. The fitness functions of tensile stress and strain are established based on four machine learning models with the hyperparameters tuned by the Hunger Games search (HGS) algorithm. After the data augmentation, the values of in their test sets are increased from 0.874 to 0.925 and from 0.772 to 0.889, respectively. Subsequently, the third objective function (cost) is computed by polynomials and four classes of constraints (Min-max, volume, ratio, and fiber) are set up to define the variable’s search space. A non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is introduced to optimize the mixture proportions of ECC by simultaneously optimizing tensile stress, tensile strain, and cost. This paper combines the results of data augmentation, model prediction, and multi-objective optimization for complex ECC design, which aims to provide a basis for practical application.

Competitive pricing and seed node selection in a two-echelon supply chain

This paper presents a bi-level game model for pricing in a supply chain where the manufacturer (He) is the leader, and the retailer (She) is the follower. The leader decides on the wholesale price, and the follower decides on the selling price and selects seed nodes. The main idea of the model is that the marketing strategy used for promoting the product is focused on giving free samples to potential customers. Hence, the importance of analyzing a social network becomes evident. To maximize her profit, the retailer decides based on three factors: first, the leader's decision about wholesale price; second, the social network structure, which is critical for selecting the seed nodes; and third, people's valuation of the product. Therefore, a bi-level Mixed-Integer Nonlinear Programming model is developed to consider the social binds between potential customers. To solve this model, we employ a meta-heuristic algorithm. Finally, the effect of the model's parameters on decision variables and the objective functions is discussed. Based on the analysis and discussions, the production cost has a prominent impact on the players’ decisions and profits. Furthermore, instead of spending all the marketing budget on increasing seed nodes, it is suggested that they be spent on market research and improving good publicity. Moreover, deciding whether the players want to maximize the profit or market penetration is required before diving into decision-making.