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To determine optimum design and operation parameters for ternary batch distillation under fixed product demand.

In this study, two different scenarios are considered. In the first scenario, the column specification (in terms of number of plates and vapour load) and product demand are given and the optimum operation policy is determined. In the second scenario, with a fixed batch time and product demand, the optimal design (in terms of number of plates and vapour load) and operation policy (in terms of reflux ratio profile) are determined. In both scenarios, maximisation of a profit function reflecting capital cost, operating cost and penalty due to under/over production and customer dissatisfaction is considered. A detailed dynamic model consisting of mass and energy balances with rigorous thermodynamic property calculation model is used. The optimisation problem is solved using modified simulated annealing algorithm.

Two ternary mixtures leading to easy and difficult separation were considered. The off‐cut production and recycling has been found to be more beneficial for difficult separation mixture than that for easy separation mixture. The net profit increases with over production more than under production of the products. This is because of the penalty imposed for customer dissatisfaction. It is better to over produce, as that will achieve the maximum profit and (at the same time) satisfy the customer. Finally, for a typical case study, the net profit with optimum design is found to be about 25 per cent more compared to the net profit obtained with fixed design.

Optimal design and operation of multicomponent batch distillation has received limited attention in the past. Also, these studies were not geared for fixed product demand scenario. The optimisation problem formulation considered in the past, led to unlimited production of products (based on the assumption that all products produced are saleable) to maximise the profitability. Also, there were no penalties for over or under production of the desired products, production of off‐cuts and customer dissatisfaction due to not meeting the order (amount of products and delivery time, etc.). In this work, for the first time, these issues are addressed in the optimisation problem formulation.

The purpose of this paper is to present the modeling of statistical variation of experimental data using the design of experiments method to optimize the formulation of a high performance concrete (HPC) using materials that are locally available in Algeria. For this, two mineral additions (natural pozzolana and limestone filler [LF]) were used. Both additions are added by substitution of cement up to 25%. To better appreciate the effect of replacing a part of cement by natural pozzolana and LF and to optimize their combined effect on the characteristics of HPC, an effective analytical method is therefore needed to reach the required objective.

The experimental part of the study consisted of substituting a portion of cement by various proportions of these additions to assess their effects on the physico-mechanical characteristics of HPC. A mixture design with three factors and five levels was carried out. The JMP7 software was used to provide mathematical models for the statistical variation of measured values and to perform a statistical analysis. These models made it possible to show the contribution of the three factors and their interactions in the variation of the response.

The mixture design approach made it possible to visualize the influence of LF and pozzolanic filler (PF) on the physico-mechanical characteristics of HPC, the developed models present good correlation coefficients (R² = 0.82) for all studied responses. The obtained results indicated that it is quite possible to substitute a part of cement with LF and PF in the formulation of a HPC. Thanks to the complementary effect between the two additions, the workability could be improved and the strengths drop could be avoided in the short, medium and long term. The optimization of mixture design factors based on the mathematical models was carried out to select the appropriate factors combinations; a good agreement between the experimental results and the predicted results was obtained.

The coefficient of PF in Cs₂₈ model is closer to that of LF than in Cs₇ model, thanks to the complementary effect between LF and PF at the age of 28 days. It was found that the optimal HPC14 concrete (10%LF–5%PF) provides the best compromise between the three responses. It is also worth noting that the use of these two local materials can reduce the manufacturing costs of HPC and reduce carbon dioxide emissions into the atmosphere. This can be an important economic and environmental alternative.

This study comprehensively examines entropy generation and thermosolutal performance of a ternary hybrid nanofluid in a partially active porous cabinet. The purpose of this study is to comprehend the intricate phenomena of double diffusion by investigating the dispersion behavior of Al₂O₃, CuO, and Ag nanoparticles in water.

The cabinet design consists of two horizontal walls and two curved walls with the lower border divided into a heated and concentrated region of length b and the remaining sections are adiabatic. The vertical borders are cold and low concentration, while the upper border is adiabatic. Two cavity configurations such as convex and concave are considered. A uniform porous medium is taken within the ternary hybrid nanofluid. This has been characterized by the Brinkman-extended Darcy model. Thermosolutal phenomena are governed by the Navier-Stokes equations and are solved by adopting a higher-order compact scheme.

The present study focuses on exploring the influence of several well-defined parameters, including Rayleigh number, Darcy number, Lewis number, Buoyancy ratio number, nanoparticle volume concentration and heater size. The results indicate that the ternary hybrid nanofluid outperforms both the mono and hybrid nanofluids in all considered aspects.

This study brings forth a significant contribution by uncovering novel flow features that have previously remained unexplored. By addressing a well-defined problem, the work provides valuable insights into the enhancement of thermal transport, with direct implications for diverse engineering devices such as solar collectors, heat exchangers and microelectronics.

The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.

First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.

The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.

Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

The purpose of this paper is to implement the mathematical models to predict concretes physico-mechanical characteristics made with binary and ternary sands using a mixture design method. It is a new technique that optimizes mixtures without being obliged to do a lot of experiments. The goal is to find the law governing the responses depending on mixture composition and capable of taking into account the effect of each parameter separately and in interaction between several parameters on the characteristics studied.

Mixture design method was used for optimizing concretes characteristics and studying the effects of river sand (RS), dune sand (DS) and crushed sand (CS) in combinations of binary system and ternary on workability, the compressive and flexural strengths of concretes at 7 and 28 days. A total of 21 mixtures of concrete were prepared for this investigation. The modeling was carried out by using JMP7 statistical software.

Mixture design method made it possible to obtain, with good precision, the statistical models and the prediction curves of studied responses. The models have relatively good correlation coefficients (R² = 0.70) for all studied responses. The use of binary and ternary mixtures sands improves the workability and their mechanical strengths. The obtained results proved that concrete, based on binary mixture C15, presents the maximum compressive strength (MCS) on 28 day with an improvement of around 20%, compared to reference concrete (C21). For ternary mixtures, MCS on 28 day was obtained for the mixture C10 with an improvement of around 15% compared to C21. Increase in compressive strength during the progress of hydration reactions was accompanied by an increase in the flexural strength, but in different proportions.

The partial incorporation of DS (= 40%) in the concrete formulation can provide a solution for some work in the southern regions of country. In addition, the CS is an interesting alternative source for replacing 60% of RS. The concrete formulation based on local materials is really capable of solving the economic and technical problems encountered in the building field, as well as environmental problems. Local resources therefore constitute an economic, technological and environmental alternative.

This work aims to concentrate on the mixed convection of the stagnation point flow of ternary hybrid nanofluids towards vertical Riga plate. Aluminum trioxide (Al₂O₃), silicon dioxide (SiO₂) and titanium dioxide (TiO₂) are regarded as nanoparticles, with water serving as the base fluid. The mathematical model incorporates momentum boundary layer and energy equations. The Grinberg term for the viscous dissipation and the wall parallel Lorentz force coming from the Riga plate are taken into consideration in the context of the energy equation.

Through the use of appropriate nonsimilar transformations, the governing system is transformed into nonlinear nondimensional partial differential equations (PDEs). The numerical method bvp4c (built-in package for MATLAB) is used in this study to simulate governing equations using the local non-similarity (LNS) approach up to the second truncation level.

Numerous graphs and numerical tables expound on the physical properties of the nanofluid temperature and velocity profiles. The local Nusselt correlations and the drag coefficient for pertinent parameters have been computed in tabular form. Additionally, the temperature profile drops while the velocity profile increases when the mixed convection parameter is included to oppose the flow.

The fundamental goal of this work is to comprehend how ternary nanofluids move towards a vertical Riga plate in a mixed convective domain with stagnation point flow.

The purpose of this paper is to study haemodynamic flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, magnetohydrodynamic (MHD) flow and aneurysm conditions. The findings of this study offer significant insights into the intricate interplay encompassing electro-osmosis, MHD flow, microorganisms, Joule heating and the ternary hybrid nanofluid.

The governing equations are first non-dimensionalised, and subsequently, a coordinate transformation is used to regularise the irregular boundaries. The discretisation of the governing equations is accomplished by using the Crank–Nicolson scheme. Furthermore, the tri-diagonal matrix algorithm is applied to solve the resulting matrix arising from the discretisation.

The investigation reveals that the velocity profile experiences enhancement with an increase in the Debye–Hückel parameter, whereas the magnetic field parameter exhibits the opposite effect, reducing the velocity profile. A comparative study demonstrates the velocity distribution in Au-CuO hybrid nanofluid and Au-CuO-GO ternary hybrid nanofluid. The results indicate a notable enhancement in velocity for the ternary hybrid nanofluid compared to the hybrid nanofluids. Moreover, an increase in the Brinkmann number results in an augmentation in entropy generation.

This study investigates the flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, MHD flow and aneurysm conditions. The governing equations are non-dimensionalised, and a coordinate transformation is applied to regularise the irregular boundaries. The Crank–Nicolson scheme is used to model blood flow in the presence of a ternary hybrid nanofluid (Au-CuO-GO/blood) within the arterial domain. The findings shed light on the complex interactions involving stenosis, MHD flow, aneurysms, Joule heating and the ternary hybrid nanofluid. The results indicate a decrease in the wall shear stress (WSS) profile with increasing stenosis size. The MHD effects are observed to influence the velocity distribution, as the velocity profile exhibits a declining nature with an increase in the Hartmann number. In addition, entropy generation increases with an enhancement in the Brinkmann number. This research contributes to understanding fluid dynamics and heat transfer mechanisms in bifurcated arteries, providing valuable insights for diagnosing and treating cardiovascular diseases.

The purpose of this paper is to enhance the understanding on the electrodeposition of various lead (Pb)‐free solder alloys, so that new studies can be carried out to solve processing issues.

The paper reviews the available reports on the electrodeposition of tin (Sn)‐based solder systems and identifies the challenges in this area.

Compositional control remains a major challenge in this area, where the achievement of desired composition for binary and ternary alloys is subjected to uncertainties. The use of chelating agents in the bath and optimization of parameters can assist the achievement of near‐desired alloy composition. Acidic plating baths are preferred due to their compatibility with photoresists but oxidation of stannous ions causes poor bath stability. Antioxidants, reducing agents and low oxygen overpotential anodes can suppress the oxidation rate and increase the lifespan of plating baths. Apart from chelating agents and antioxidants, various categories of additives can be added to improve quality of deposits. Surfactants, grain refiners and brighteners are routinely used to obtain smooth, fine‐grained and bright deposits with good thermo‐mechanical properties.

The paper provides information on the key issues in electrodeposition of Pb‐free solder alloys. Possible measures to alleviate the issues are suggested so that the electrodeposition technique can be established for mass production of a wider range of solder alloys.