This paper aims to optimize the quality characteristics of the drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing the drilling parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects of drilling process parameters on machinability were analyzed by evaluating the machinability characteristics. The cutting power was modeled through drilling parameters (speed and feed), drill point angle, and laminate thickness. The response surface analysis and artificial neural networks enhanced by the particle swarm optimization algorithm were applied for modeling and evaluating the effect of process parameters on the machinability of the drilling process. The most influential parameters on machinability properties and delamination were determined by analysis of variance (ANOVA). A multi-response optimization was performed to optimize drilling process parameters for sustainable drilling quality cha... [more]

The present study aimed to optimize the process parameters (milling depth and advanced speed) for an asphalt milling operation using a multi-response approach based on Taguchi design of experiments (DOE) and Grey Relational Analysis (GRA). Nine simulations tests were conducted using Discrete Element Method (DEM) in order to determine the forces acting on the cutting tooth support and tip. The considered performance characteristics were cutting forces (smaller is better category) and chip section area (larger is better category). A Grey Relational Grade (GRG) was determined from GRA, allowing to identify the optimal parameter levels for the asphalt milling process with multiple performance characteristics. It was found that that the optimal milling parameters for multi-response analysis are a milling depth of 200 mm and an advanced speed of 30 mm/min. Furthermore, analysis of variance (ANOVA) was used to determine the most significant factor influencing the performance characteristics.... [more]

Phosphatidylserine (PS) has significant biological and nutritional effects and finds wide applications in the food, pharmaceutical, and chemical industries. To produce high-value PS efficiently, phospholipase D (PLD)-induced transphosphatidylation of low-value phosphatidylcholine (PC) with L-serine has been explored. In this research, we purified recombinant PLD from Streptomyces antibioticus SK-3 using ion exchange chromatography and gel filtration chromatography. Subsequently, we thoroughly characterized the purified enzyme and optimized the transphosphatidylation conditions to identify the most favorable settings for synthesizing PS in a biphasic system. The purified recombinant PLD displayed a robust transphosphatidylation function, facilitating efficient catalysis in the synthesis of PS. Under the optimal conditions (butyl acetate/enzyme solution 1:1, L-serine 160 mg/mL, soybean lecithin 2 mg/mL, and MgCl2 15 mM, at 50 °C for 2.5 h with shaking), we achieved a conversion rate of 9... [more]

Nanofiltration is well suited to separate monovalent ions from multivalent ions, such as the separation of Li+ and Mg2+ from seawater, a potential lithium source for the production of lithium-ion batteries. To the best of our knowledge, there is no existing work on the optimization of a multi-stage membrane plant that differentiates between different ions and that is based on a validated transport model. This study presents a method for modeling predefined membrane interconnections using discretization along the membrane length and across the membrane thickness. The solution-diffusion−electromigration model was used as the transport model in a fundamental membrane flowsheet, and the model was employed to optimize a given flowsheet with a flexible objective function. The methodology was evaluated for three distinct separation tasks, and optimized operating points were found. These show that permeances and feed concentrations might cause negative rejections and positive rejections (espec... [more]

Parallel flow double and triple-effect vapor absorption cooling systems (VACS) are trying to meet the challenges of vapor compression cooling systems due to their better performance. Therefore, the present study deals with the review, thermodynamic analysis, and optimization of operating parameters for both double and triple-effect VACS. Lithium bromide water was selected as the working fluid, while liquified petroleum gas (LPG) and compressed natural gas (CNG) were taken as the source of energy to drive both the VACS. Detailed First Law analysis, i.e., coefficient of performance (COP), was examined along with the optimization of operating parameters (such as salt concentration and operating generators temperature at different pressure levels) and the volume flow rate of the gases. Optimization was carried out for maximum COP of the VACS using an iterative technique. Our results show that the COP of the triple-effect system was approximately 32% higher than the double effect, while 15−... [more]

The research presented aimed to address the treatment of fluid waste with significant environmental impact by utilizing plasma technology, specifically plasma arc flow (PAF). The goal was to develop a novel purification material based on graphene for industrial applications and to optimize the treatment process. Analysis and monitoring of a submerged arc plasma reactor were the main goals of this research. This entailed a careful examination of the incoming wastewater that needed to be treated with the goal of identifying its precise composition characteristics with the relative tolerances needed for the reactions that were to follow in the reactor. The focus of the analysis was on input-parameter optimization, production of characteristic curves, and analysis of the factors affecting hydrogen evolution in syngas. Additionally, the study investigated how to determine the best viscosity for a particular input matrix by carrying out an evaluation study. The effects of this parameter were... [more]

Research on cannabis oil has evolved to encompass the pharmaceutical industry for the therapeutic potential of the active compounds for pathologies such as Alzheimer, auto-immune disorders, and cancer. These debilitating diseases are best treated with cannabinoids such as tetrahydrocannabinol (∆9-THC), cannabigerol (CBG), and cannabinol (CBN), which relieve neuropathic pain and stimulate the immune system. We extracted cannabinoids from plants with supercritical CO2 and produced an extract with a total yield close to 26%. The three-level Box−Behnken experimental design considered four factors: Temperature, pressure, CO2 flow rate, and processing time, with predetermined parameters at low, medium, and high levels. The mathematical model was evaluated by regression analysis. The yield of ∆9-THC and CBG reached a maximum after 2 h and 15 g/min of CO2, 235 bar, 55 °C (64.3 g THC/100 g of raw material and 4.6 g CBG/100 g of raw material). After another 2 h of extraction time, the yield of C... [more]

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are made using two approaches: (i) an optimization approach and (ii) a rule-based approach, which are followed by a comparative study. An MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the oth... [more]

Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that t... [more]

Gas-To-Liquid (GTL) processing involves the conversion of natural gas to liquid hydrocarbons that are widely used in the chemical industry. In this process, the Fischer−Tropsch (F-T) approach is utilized and, as a result, wastewater is produced as a by-product. This wastewater commonly contains alcohols and acids as contaminants. Prior to discharge, the treatment of this wastewater is essential, and biological treatment is the common approach. However, this approach is not cost effective and poses various waste-related issues. Due to this, there is a need for a cost-effective treatment method. This study evaluated the adsorption performance of activated carbon fibers (ACFs) for the treatment of GTL wastewater. The ACF in this study exhibited a surface area of 1232.2 m2/g, which provided a significant area for the adsorption to take place. Response surface methodology (RSM) under central composite design was used to assess the effect of GTL wastewater’s pH, initial concentration and dos... [more]

The intermediate fluid thermoelectric generator (IFTEG) represents a novel approach to power generation, predicated upon the principles of gravity heat pipe technology. Its key advantages include high-power output and a compact module area. The generator’s performance, however, is influenced by the variable exhaust parameters typical of automobile operation, which presents a significant challenge in the design process. The present study establishes a mathematical model to optimize the design of the IFTEG. Our findings suggest that the optimal module area sees substantial growth with an increase in both the exhaust heat exchanger area and the exhaust flow rate. Interestingly, the optimal module area appears to demonstrate a low sensitivity to changes in exhaust temperature. To address the challenge of determining the optimal module area, this study introduces the concept of peak power deviation. This method posits that any deviation from the optimal module area results in an equivalent... [more]

Heat integration by a heat exchanger network (HEN) is an important topic in chemical process system synthesis. From the perspective of optimization, the simultaneous synthesis of HEN belongs to a mixed-integer and nonlinear programming problem. Both the stage-wise superstructure (SWS) model and the chessboard model are the most widely adopted and belong to structural models, in which a framework is assumed for stream matching, and the global optimal solution outside its feasible domain may be defined by the framework. A node-wise non-structural model (NW-NSM) is proposed to find more universal stream matching options, but it requires a mass of structural variables and extra multiple correction strategies. The aim of this paper is to develop a novel matrix non-structural model (M-NSM) for HEN without stream splits from the perspectives of global optimization methods and superstructure models. In the proposed M-NSM, the heat exchanger position order is quantized by matrix elements at eac... [more]

Class and shape transformation functions are proposed to carry out the parametric design of the blade profiles because fan efficiency is closely related to the shape of blade profiles. An optimization with the objectives of fan efficiency and static pressure based on the Kriging models was established, and numerical simulation data were applied to construct the Kriging models. The dissipation function was used to analyze the fan energy loss. The prediction results show that the maximum accuracy error between the Kriging model and the experimental data is approximately 0.81%. Compared with the prototype fan, the optimized fan was able to ameliorate the distribution of the flow field pressure and velocity; the outlet static pressure increased by 9.03%, and the efficiency increased by 2.35%. The dissipation function is advantageous because it can intuitively indicate the location and amount of energy loss in the fan, while effectively obtaining the total energy loss as well. The situation... [more]

Jacq., from the Yucatan peninsula, is recognized worldwide for its pungency, flavor, and secondary metabolites content. This has resulted in an increase in its production, which has led to an increase in the number of byproducts considered waste, mainly its leaves. Capsicum chinense leaves have been demonstrated to contain polyphenols with bioactive properties (antioxidant, anti-inflammatory, antiobesogenic capacity, etc.); hence, the extraction of polyphenols through the use of natural deep eutectic solvents (NADES) with a green technology, such as an ultrasonic probe, could help to revalue these leaves by maximizing the extraction efficiency and preserving their bioactive properties. The objective of this study was to optimize the composition of a eutectic solvent for obtaining an extract rich in polyphenols from the Capsicum chinense leaf using a sonic probe. The optimum conditions of the composition of NADES for obtaining the highest Antioxidant capacity (Ax, 79.71% inhibition) wer... [more]

Due to increased demands of production capacity and higher quality requirements, industries are automating at a fast pace. Industrial robots are an important component of the industrial automation ecosystem. However, the selection of appropriate robots is a challenging task due to the sheer number of alternatives present and their varied specifications. The various characteristics or attributes of industrial robots that need due consideration before selection of an optimal robot for a given application are found to be conflicting in nature. Thus, in this paper, several multi-criteria decision-making (MCDM) methods are deployed to select an optimal robot depending on the application. Three different industrial robot selection problems are solved in this paper by using Simple Additive Weighing (SAW), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Linear Programming Technique (LINMAP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Elimi... [more]

In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (S... [more]

The yields of chemical reactions are highly dependent on the mixing pattern between reactants. Herein, we report the modification of a meso-micromixing interaction reaction model which is applied in batch reactors by leveraging the flow characteristics in the continuous reactors. Both experimental and model-predicted yields were compared using the classical Villermaux−Dushman method in a self-designed split and recombination reactor. This modified model significantly reduced the error in predicted product yields from approximately 15% to within 3%, compared to a model containing the micromixing term only. The effects of flow rates and reactor structure parameters on mixing performance were analyzed. We found that increasing flow rates and the degree of twist in the mixing element’s grooves, as well as decreasing the cross-sectional area of grooves, improved mixing performance. The optimization of reactor flow rates and structural parameters was achieved by combining Gaussian process re... [more]

Renewable energy technologies and resources, particularly solar photovoltaic systems, provide cost-effective and environmentally friendly solutions for meeting the demand for electricity. The design of such systems is a critical task, as it has a significant impact on the overall cost of the system. In this paper, a mixed-integer linear programming-based model is proposed for designing an integrated photovoltaic-hydrogen renewable energy system to minimize total life costs for one of Saudi Arabia’s most important fields, a greenhouse farm. The aim of the proposed system is to determine the number of photovoltaic (PV) modules, the amount of hydrogen accumulated over time, and the number of hydrogen tanks. In addition, binary decision variables are used to describe either-or decisions on hydrogen tank charging and discharging. To solve the developed model, an exact approach embedded in the general algebraic modeling System (GAMS) software was utilized. The model was validated using a far... [more]

The following paper examines the practicality of a methodical approach for energy-flexible and energy-optimal operation in the field of metal-cutting production. The analysis is based on the example of a grinding machine and its central cooling-supply system. In the first step, an energy-flexibility data model is built for each subsystem, which describes energy flexibility potentials generically. This is then extended to enable combined energy cost-optimal production planning. As a basis for the links between the data model representations, the cold flows between the subsystems are modeled using parameter-estimation methods, which have a mean absolute error of only 2.3 percent, making the subsequent installation of heat meters unnecessary. Based on the presented approach, the results successfully validate the possibility of energy-flexible cost-optimal and sensor-reduced production planning by reducing energy costs by 6.6 percent overall and 1.9 percent per workpiece produced.

Standard technical solutions are not cost-effective for many small hydropower sites. This study aims to demonstrate the workflow for the tailor-made variable-speed axial propeller turbine and provide proof of this concept. The turbine is designed to meet the site’s specific space limitations and operating range needs. The runner shape is adjusted to the variable-speed operation and defined hydraulic profile using a parametric geometry model and CFD-based optimization. The variable-speed propeller turbine shows excellent flow control while keeping the mechanical design simple. The tailor-made approach minimizes construction costs using existing structures and is highly suitable for mini-hydropower applications. The prototype—an atypical turbine designed for highly restricted space and installed on-site—serves as proof of the concept. The findings on the design of axial variable-speed turbines are presented.

A smart hybrid energy system (SHES) is presented using a combination of battery, PV systems, and gas/diesel engines. The economic/environmental dispatch optimization algorithm (EEDOA) is employed to minimize the total operating cost or total CO2 emission. In the face of the uncertainty of renewable power generation, the constraints for loss-of-load probability (LOLP) and the operating reserve for the rechargeable battery are taken into account for compensating the imbalance between load demand and power supplies. The grid-connected and islanded modes of SHES are demonstrated to address a low-carbon community. For forecasting load demand, PV power, and locational-based marginal pricing (LBMP), the proper forecast model, such as long short-term memory (LSTM) or extreme gradient boosting (XGBoost), is implemented to improve the EEDOA. A few comparisons show that (i) the grid-connected mode of SHES is superior to the islanded-connected mode of SHES due to lower total operating cost and les... [more]

Recently, renewable energy resources (RESs) have been utilized to supply electricity to remote areas, instead of the conventional methods of electrical energy production. In this paper, the optimal design of a standalone hybrid RES comprising photovoltaic (PV), wind turbine (WT), and biomass sources as well as an energy storage system, such as a hydro-pumped storage system, is studied. The problem of the optimal sizing of the generating units in the proposed energy system is formulated as an optimization problem and the algorithms heap-based optimizer (HBO), grey wolf optimizer (GWO), and particle swarm optimization (PSO) are applied to achieve the optimal sizing of each component of the proposed grid-independent hybrid system. The optimization problem is formulated depending on the real-time meteorological data of the Ataka region on the Red Sea in Egypt. The main goal of the optimization process is to minimize the cost of energy (COE) and the loss of power supply probability (LPSP),... [more]

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency... [more]

The Habanero pepper (Capsicum chinense Jacq.) is recognized worldwide for its unique organoleptic characteristics, as well as for its capsaicin content; however, other bioactive compounds, such as phenolic compounds with bioactive properties (mainly antioxidant capacity), have been extracted (ultrasound) and identified in this fruit. Moreover, the extracts obtained by ultrasound present a high sensitivity to environmental conditions, making spray drying a viable option to avoid the degradation of bioactive compounds while maintaining their properties after microencapsulation. Response surface methodology (RSM) has been used to optimize spray-drying conditions such as the inlet temperature (IT) and maltodextrin:extract (M:E) ratio. Thus, the objective of this work was to establish the optimal spray-drying conditions (IT and M:E) of a Habanero pepper extract with a final characterization of the spray-dried product. Results showed that the optimal spray-drying conditions included an IT =... [more]

Chinalco’s Toromocho mine, located in the Morococha district of Peru, treated 117,200 mtpd ores during last 10 years. It is currently undergoing expansion, treating approximately 52,740 mtpd of chalcopyrite ore since 2021. As with the commissioning of most large plants, the metallurgical performance levels produced after stability was achieved were below the design criteria; more specifically, the Cu overall recoveries were 80−82% compared with the design value of 85%, and the final concentrate Cu grades were 20−23% compared with the design value of 24% over the past 2 years. It is clear that the copper losses in the fine size fraction (<10 µm) were due to unoptimized hydrodynamics. To overcome this obstacle and improve overall performance, an innovative approach for flotation circuit evaluation was designed and set up, to improve understanding of the nature of the mineral losses, benchmark the cell hydrodynamics as a platform to improve flotation cell operation, and link the circui... [more]