Pathway development for brewer's spent grain valorization using multi-objective optimization.

Multi-source weighted domain adaptation guided mechanical cross-domain diagnosis method with cross-layer hybrid attention and multi-objective optimization.

CFD-based and machine learning-assisted optimization of screw geometry in food extrusion processes

• Multi-objective optimization applied to achieve NZEB in Prague. • Integration of passive, active, and renewable strategies. • Energy savings drop from 70 % to 32 % under future climates. • Emphasis on adaptive, resilient building designs. • NZEB strategies need regular optimization for future climates. This study applies a Multi-Objective Optimization (MOO) framework to enhance the energy performance of a typical high-rise residential building in Prague and assess its resilience under future climate change scenarios. A total of 18 passive strategies—including external wall and roof insulation (0.08–0.20 m), optimized Window-to-Wall Ratios (10–90 % by orientation), triple low-E glazing, and dynamic shading overhangs—were evaluated alongside two active strategies (heating and cooling COP optimization) and three renewable strategies (roof-mounted photovoltaic (PV) panels with tilt, spacing, and orientation optimization). Results show that integrating these strategies can reduce annual energy demand by up to 68.7 % under current climate conditions and by 56 %, 42 %, and 32 % in the 2020s, 2050s, and 2080s, respectively. The optimized configuration maintained acceptable indoor comfort, with Predicted Percentage Dissatisfied (PPD) values reduced by up to 18 % compared to the baseline. PV panels generated 53,875 kWh annually under current conditions, covering 69 % of thermal and electrical demand, though coverage declined under future climate scenarios due to rising cooling loads. These findings demonstrate that a comprehensive integration of passive, active, and renewable strategies can significantly improve building performance and provide a resilient pathway toward Near-Zero Energy Buildings (NZEBs) under changing climatic conditions.

Enhancing hybrid renewable system performance through load shifting: A multi-objective optimization and forecasting approach

The thermo-hydrodynamic (THD) lubrication performance of journal bearings is critical to the operational stability and service life of rotating machinery. To achieve a breakthrough in enhancing the THD lubrication performance of journal bearings through surface texturing, we design a novel wing-shaped texture inspired by the falcon's superior flow-guiding and pressure-converging characteristics. A three-dimensional multiphase CFD THD model is established to investigate the influence of wing-texture parameters. The wing-shaped texture is compared with commonly used textures, including rectangle, circle, and triangle designs. To address the complex geometry of the wing-shaped texture, the excessive number of design parameters, and the conflicting objectives, a quadratic response surface model is constructed using central composite design to map parameters to objectives. Multi-objective optimization is then performed by combining NSGA-II with an entropy-weighted TOPSIS method, ultimately identifying the parameter combination that yields optimal THD performance for wing-textured journal bearings. The main findings are as follows: (1) The THD performance of the wing-shaped texture is superior to that of the other three traditional textures. The coefficient of friction can be improved by up to 32.4%, and the average oil-film temperature rise is reduced by 1.08 K. (2) Placing the wing-shaped texture in the middle of the oil film's converging wedge, together with a higher circumferential placement density, delivers better THD performance. (3) After multi-objective optimization, the wing-shaped textures are concentrated at the center of the oil-film high-pressure region, thereby thickening the local film, reducing shear stress and friction, and enhancing lubricant flow and heat dissipation to suppress temperature rise. Consequently, the friction force is reduced by up to 59.3%, the load-carrying capacity increases by 20.5%, and the peak oil-film temperature decreases by 2.19 K. • Wing-shaped textures couple flow guidance and pressure convergence, cutting COF by 32.4% and oil-film rise by 1.08 K. • A 3D CFD-THD model links local flow features to pressure-temperature coordination and texture optimization. • CCD-NSGA-II-TOPSIS optimization cuts friction by 59.3%, raises LCC by 20.5%, and lowers T max by 2.19 K.

Thermal-hydraulic analysis and multi objective optimization in double-layered wavy microchannel heat sinks with combining porous ribs

Multi-objective salp swarm optimization for the sizing of a hybrid renewable energy system

Actionable counterfactual explanation generation via multi-objective optimization

Energy optimization of smart home in electrical microgrids considering economic and technical multi-objective functions and demand response programs

Multi-objective optimization and energy loss characteristics analysis of multistage centrifugal pump as turbine based on response surface methodology and MOPSO algorithm

A novel multi-objective optimization framework using NSGA-II for gene co-expression network inference.

Cavitation-enhanced carbonation for nano-ZnO synthesis via an ultrasonic-jet coupled reactor: Machine learning prediction and multi-objective optimization using a genetic algorithm.

Joint flood control operation functions for reservoir groups based on the concept of affiliated reservoirs

Climate-resilient urban housing in sub-Saharan Africa: A decision support framework integrating cultural behavior modeling with multi-objective optimization

Separation of propylene–propane mixtures requires substantial investment and energy consumption due to their similar molecular sizes and physical properties. Although many alternative processes have been investigated to replace distillation, none have demonstrated sufficient superiority over distillation columns in industrial practice. This study aimed to enhance both the energy efficiency and inherent process safety of an existing distillation unit without additional capital investment. A multi-objective optimization framework based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to identify optimal trade-offs between energy consumption, operating revenue, and Comprehensive Inherent Safety Index (CISI). The TOPSIS method was then used to select the most balanced operating point from the Pareto-optimal solutions. The results showed that significant improvements could be achieved through minor and controllable operational adjustments, providing a practical and directly applicable optimization strategy. Furthermore, a redesigned process configuration was proposed to increase polymer-grade propylene production, driven by the growing market demand for high-value polypropylene. The proposed redesign achieved the product shift with minimal capital modification while maintaining feasible operation. Overall, the findings highlight a cost-effective and industrially relevant approach that quantitatively integrates safety with performance improvement in existing separation systems.

Microwave drying (MWD) is a promising technique for dehydrating agricultural products due to its rapid volumetric heating, high energy efficiency, and superior preservation of product quality. However, the complex and nonlinear nature of microwave–material interactions, along with the spatial and temporal variability of dielectric properties, presents significant challenges for process modeling, control, and optimization. Traditional mathematical models often fall short in capturing these dynamics, which limits their use in adaptive or real-time process regulation. The goal of this review is to provide a comprehensive synthesis of artificial intelligence (AI) techniques applied to the microwave drying of agricultural products, focusing on predictive modeling, intelligent control, and optimization through a bibliometric analysis that covers literature from 2014 to 2024. Techniques such as artificial neural networks (ANNs), support vector machines (SVMs), adaptive neuro-fuzzy inference systems (ANFIS), and evolutionary algorithms are assessed for their effectiveness in modeling drying kinetics, predicting quality attributes, and supporting closed-loop control. Recent advancements in hybrid and ensemble models, real-time sensor integration, and multi-objective optimization are also examined. The review highlights current limitations in AI-based drying systems, including data scarcity, overfitting, poor model interpretability, and limited real-time deployment. It proposes strategic future directions, such as the adoption of explainable AI, digital twin frameworks, embedded edge computing, and sensor fusion for autonomous control. This work highlights the transformative potential of AI in developing intelligent, scalable, and energy-efficient MWD systems that align with the goals of Industry 4.0 and sustainable food engineering. • AI methods predict microwave drying behavior more effectively than classical models. • ANNs are widely used to model drying rates and moisture ratios from microwave parameters. • Hybrid AI models improve optimization of microwave power and temperature settings. • AI-driven control systems use sensor feedback and machine learning to adjust drying. • AI enables multi-objective optimization of energy use, time, and product quality.

Semi-auxetic cellular laminate: A novel lightweight stiff material

An advanced thermal storage integration framework for flexibilization of thermal power units: feasibility analysis and multi-objective optimization

An enhanced layout planning approach for metro-led underground space integrating spatial morphology and function