Topology optimization of the flat steel shear wall based on the volume constraint and strain energy assumptions under the seismic loading conditions.

In structural engineering systems, shear walls are two-dimensional vertical elements designed to endure lateral forces acting in-plane, most frequently seismic and wind loads. Shear walls come in a variety of materials and are typically found in high-rise structures. Because steel shear walls are lighter, more ductile, and stronger than other concrete shear walls, they are advised for usage in steel constructions. It is important to remember that the steel shear wall has an infill plate, which can be produced in a variety of forms. The critical zones in flat steel shear walls are the joints and corners where the infill plate and frame meet. The flat infill plate can be modified to improve the strength and weight performance of the steel shear walls. One of these procedures is Topology Optimization (TO) and this method can reduce the weight and also, increase the strength against the cyclic loading sequences. In the current research paper, the TO of the infill steel plate was considered based on the two methods of volume constraint and maximization of strain energy. Four different volumes (i.e., 60%, 70%, 80%, and 90%) were assumed for the mentioned element in the steel shear wall. The obtained results revealed that the topology configuration of CCSSW with 90% volume constraint presented the highest seismic loading performance. The cumulated energy for this type of SSW was around 700 kJ while it was around 600 kJ for other topology optimization configurations.

Forecasting the transportation energy demand with the help of optimization artificial neural network using an improved red fox optimizer (IRFO).

Transportation energy demand has a significant impact on worldwide energy consumption and greenhouse gas emissions. Accurate transportation energy demand predictions can help policymakers develop and implement successful energy policies and strategies. In this study, a novel approach to predict transportation energy demand using the Artificial Neural Network (ANN) based on the Improved Red Fox Optimizer (IRFO) has been suggested. The proposed method utilizes the ANN model to solve the complex nonlinear relationships between transportation energy demand and its effective parameters including Gross Domestic Product (GDP), population, and vehicle numbers. Also, the IRFO algorithm was utilized to modify the ANN model's parameters to improve the prediction accuracy. The experimental findings demonstrate the ANN-IRFO model performs better than the other method in terms of accuracy and effectiveness. It predicts the growth of GDP, population, and vehicles number by 5.5 %, 4.8 %, and 4.2 %, respectively. The findings demonstrate that the suggested method can provide accurate forecasts for transportation energy demand, which can help decision-makers to make informed decisions and policies regarding energy management and sustainability.

Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study.

Today, an important problem of the building energy performance area is carrying out multi-criteria optimizations of real building designs. To solve this problem, a new method based on a meta-model is proposed in this study. Hence, the EnergyPlus™ is used as the simulation tool for the performance simulation of the building, then a couple of the multi-criteria Modified Coot Optimization Algorithm (MCOA) dynamically combined with the artificial neural network meta-models (ANN-MM) are employed. For the sample generation applied for training and validation of ANN meta-models, an optimum way is presented by this method to minimize the whole building energy simulations needed for their training, and validate precise results of optimization. Moreover, the method is used for the thermal comfort and energy efficiency optimization of a real house to achieve the optimum balance between the heating and cooling behavior of the case building. 12 effective design variables of this case study are selected. Also, the achieved results are put in comparison with the "true" Pareto front found through an optimization method based on simulation performed for more validation. It is assumed that 1280 points are adequate in this case study to obtain precise results on the Pareto set. Thus, 75% of the required simulations' number based on physics has been saved by this size of sample considering the 5120 applied in the method based on simulation. Consequently, the optimum Pareto set of a real multi-criteria building efficiency optimization problem is achieved by the proposed method and accurate results are achieved.

Techno-economic analysis and optimization of near-zero energy and emission neighborhoods using biomass waste.

The present study aims to simulate and design a near-Zero Energy neighborhood in one of the most significant industrial cities for reducing greenhouse gas emissions. For this building, biomass wastes are used for energy production, and also energy storage is provided using a battery pack system. Additionally, the Fanger model is used to assess the passengers' thermal comfort, and information on hot water usage is given. The transient performance of the aforementioned building is tested for one year using TRNSYS software, which was employed for this simulation. Wind turbines are considered electricity generators for this building, and any extra energy generated is stored in a battery pack for usage when the wind speed is insufficient and electricity is needed. Hot water is created using a biomass waste system and is kept in a hot water tank after being burned using a burner. A humidifier is utilized to ventilate the building, and a heat pump provides both the building's heating and cooling needs. The produced hot water is used to supply the residents' hot water. In addition, The Fanger model is considered and used for the assessment of occupants' thermal comfort. Matlab software is a powerful software used for this task. According to the findings, a wind turbine with a 6 kW generation capacity may supply the building's power needs while also charging the batteries beyond their initial capacity, and the building will have zero energy. Additionally, biomass fuel is used to give the building the required water which should be hot. On average, 200 g of biomass and biofuel are used per hour to maintain this temperature.

Machine learning approach to predict the biofuel production via biomass gasification and natural gas integrating to develop a low-carbon and environmental-friendly design: Thermodynamic-conceptual assessment.

A hybrid energy cycle (HEC) based on biomass gasification can be suggested as an efficient, modern and low-carbon energy power plant. In the current article, a thermodynamic-conceptual design of a HEC based on biomass and solar energies has been developed in order to generate electric power, heat and hydrogen energy. The planned HEC consists of six main units: two electric energy production units, a heat recovery unit (HRU), a hydrogen energy generation cycle based on water electrolysis, a thermal power generation unit (based on LFR field), and a biofuel production unit (based on biomass gasification process). Conceptual analysis is based on the development of energy, exergy and exergoeconomic assessments. Besides that, the reduction rate of pollutant emission through the planned HEC compared to conventional power plants is presented. In the planned HEC, when hydrogen energy is not needed, excess hydrogen is feed into the combustion chamber to improve system performance and reduce the need for natural gas. Accordingly, the rate of polluting gases emitted from the cycle can be mitigated due to the reduction of fossil fuels consumption. Further, based on the machine learning technique (MLT), the level of biofuel produced from the mentioned process is estimated. In this regard, two algorithms (i.e., Support vector machine and Gaussian process regression) have been employed to develop the prediction model. The findings indicated that the considered HEC can produce about 10.2 MW of electricity, 153 kW of thermal power, and 71.8 kmol/h of hydrogen energy. In both training and testing sets, the Support vector machine model exhibits better behavior compared the two Gaussian process regression model. Based on machine learning technique, with increasing gasification pressure, the level of biofuel obtained from the process does not increase significantly.

Spotlighting of microbial electrodeionization cells for sustainable wastewater treatment: Application of machine learning.

Microbial electrodeionization cells (MECs) have been investigated for various potential applications, including the elimination of persistent pollutants, chemical synthesis, the recovery of resources, and the development of biosensors. Nevertheless, MEC technology is still developing, and practical large-scale applications face significant obstacles. This review aims to investigate MEC implementations in sustainable wastewater treatment. Ideas and concepts of MEC technology, the setup of the electrodeionization component, the membranes of MECs, the working mechanism of MECs, and the various microorganisms used in MECs are discussed. Additionally, difficulties and prospective outcomes were discussed. The goal of this review is to support scientists and engineers in fully grasping the most recent developments in MEC technologies and applications.