Bioactive compounds derived from plants are secondary metabolites that can act through various bioactivities, namely as antioxidant, antimicrobial, anti-inflammatory, anti-hypertensive, and hypoglycemic agents. Combined with the pressure generated by consumers for more natural products with beneficial effects on health, these compounds may be suitable candidates to act as preservatives in food products. For this purpose, the extraction process becomes essential for the acquisition of a quality extract with efficiency and with the desired final properties. Therefore, the main objective of this work was to perform the optimization of the extraction yield of olive leaves (Olea europaea L.), by applying response surface methodology (RSM) and employing dynamic maceration as extraction technique. Three factors were analyzed: time (F1), temperature (F2), and solvent (F3), ranging from 5 to 120 min, 25 to 100 C, and from 0 to 100% ethanol, respectively. The study used the Box Behnken design, relying on 17 individual randomized runs. The response was the dry weight of the extract (Y1), which ranged from 21.1 to 90.5 mg. The optimization studies pointed to the increase of yield with the increase of time and temperature, but inversely by applying higher time and lower temperature values and higher temperature and lower time values. The highest yield of the dry extract was achieved at 120 min (F1), 25 C (F2), and 87% (F3) of ethanol:water. Future studies will be carried out to analyze the preservative effects of incorporating olive extract in foods, as well as analysis of other response for optimizing the best food preserving extract.

Multistart Coordinate Search Filter (MCSFilter) is an optimization method suitable to find all minimizers – both local and global – of a non convex problem, with simple bounds or more generic constraints. Like many other optimization algorithms, it may be used in industrial con- texts, where execution time may be critical in order to keep a production process within safe and expected bounds. MCSFilter was first implemented in MATLAB and later in Java (which introduced a significant performance gain). In this work, a comparison is made between these two implementations and a novel one in C that aims at further performance improvements. For the comparison, the problems addressed are bound constraint, with small dimension (between 2 and 10) and multiple local and global solutions. It is possible to conclude that the average time execution for each problem is considerable smaller when using the Java and C implementa- tions, and that the current C implementation, though not yet fully optimized, already exhibits a significant speedup.

Biodiesel is an alternative diesel fuel which is industrially produced from vegetable oils and animal fats, by transesterification reactions. Thus, currently most commercial biodiesel is produced from oils, using alkaline catalysts. On the other hand, conventional mineral acid catalysts like sulfuric acid, are commonly used for catalyzing esterification reactions of fatty acids which also produce biodiesel. However, Ionic Liquids (ILs) offer an alternative solution to classical homogeneous catalysts, because it can be recycled and reused in subsequent runs after recovery steps. An IL is a molten salt consisting of a cation and an anion, with low melting temperature. In this study, a Brønsted acidic IL, 1-butyl-3-methylimidazolium hydrogen sulfate ([BMIM][HSO4]) was used as a catalyst in the esterification of oleic acid with methanol into biodiesel. The effect of different operation parameters such as oleic acid to methanol molar ratio, amount of catalyst, reaction temperature, and reaction time were tested. The optimal conditions for esterification of oleic acid were experimentally determined at 1/10 for oleic acid/methanol molar ratio, 2.5 %wt for the amount of catalyst, a reaction time of 4 h and reaction temperature of 90oC. The Fatty Acid Methyl Esters (FAMEs) content was analyzed using Gas Chromatography.

The main aim of the present work was to optimize mead production using Response Surface Methodology. The effects of temperature (x1: 20–30 ºC) and nutrients concentration (x2: 60–120 g/hL) on mead quality, concerning the final concentrations of glucose (Y1), fructose (Y2), ethanol (Y3), glycerol (Y4) and acetic acid (Y5), were studied. Twelve operational conditions were tested. No delays and moods were observed during fermentations. The second order polynomial models determined produced satisfactory fittings of the experimental data with regard to glucose (R2= 0.646, p = 0.001), ethanol (R2= 0.741,p = 0.049), glycerol (R2= 0.899, p = 0.002), fructose (R2= 0.902, p = 0.033) and acetic acid (R2= 0.913,p = 0.001). The optimum extraction conditions determined in order to maximize the combined responses were 24 ºC and a nutrients concentration of 0.88 g/L. The mead produced under these conditions had the following characteristics: ethanol concentration of 10.2%, acetic acid 0.54 g/L, glycerol 7.8 g/L, glucose 1.8 g/L and fructose 2.5 g/L. These values were in agreement with the predicted and were within the safe limit established for acetic acid and the recommended range for glycerol. Furthermore, the residual sug- ars concentration was also low, decreasing the possibility of occurring undesirable refermentations.

In this work, we study the discrete lot sizing and scheduling problem (DSLP) in identical parallel resources with (sequence-independent) setup costs and inventory holding costs. We propose a Dantzig-Wolfe decomposition of a known formulation and describe a branch-and-price and column generation procedure to solve the problem to optimality. The results show that the lower bounds provided by the reformulated model are stronger than the lower bounds provided by the linear programming (LP) relaxation of the original model.

The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.

SAPO-34 nanocrystals were prepared and used as inorganic filler in polyethersulfone (PES) membranes. The influence of preparation conditions on permeation properties of CO2 and N2 gases and CO2/N2 selectivity were studied to achieve the highest separation performance possible for this mixed-matrix membrane. Gas permeation properties of PES/SAPO-34 MMMs with different contents of SAPO-34, PES and initial film thickness were investigated according to a full factorial design of experiments (DOE). The permeation tests and DOE results revealed that the optimized conditions to maximize the selectivity for PES/SAPO-34 are: composition of 18.5 wt % of SAPO-34, 15 wt% of PES, and a casted film thickness of 400 μm resulting in a CO2/N2 selectivity of 30.85. The findings showed that a multivariable model was validated and able to predict the selectivity with determination coefficient R2 of 0.969 and the results are in accordance with values presented in literature for PES/ SAPO-34 membranes. An economic analysis shows that the commercial grade price for the optimized membrane is EUR 9.43/m2 which represents around 2.2 times cheaper than polymeric commercial membranes.

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The presented paper addresses a comparison between two optimization methods, the Stretched simulated annealing (SSA) and the Genetic Algorithm (GA), that runs in an accurate and stable simulation model. Final results show the comparative study and proves the gait optimization technique.

Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

The fish canning industry generates large volumes of wastewater for which the treatment is particularly difficult due to the high content of organic matter and salts and to the significant amount of oil and grease they present. In this work, a closed jacketed batch reactor was used to study the feasibility of applying a Fenton reaction step after an activated sludge biological treatment. For this purpose and in order to find optimal conditions, a 33 Box-Behnken full factorial design was used. The predicted optimum value (63% DOC degradation) was found for hydrogen peroxide concentration of 1558 mg/L, iron concentration of 363 mg/L and pH 3.2. © 2014 Elsevier Ltd. All rights reserved.

A challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization

This paper presents a study of Taguchi design application to optimize surface quality in a CNC milling operation. Maintaining good surface quality usually involves additional manufacturing cost or loss of productivity. The Taguchi design is an efficient and effective experimental method in which a response variable can be optimized, given various control and/or noise factors, using fewer resources than a factorial design. This study includes feed per tooth, cutting speed and radial depth of cut as control factors. An orthogonal array of L9(34) was implemented and the ANOVA analyses were carried out to identify the significant factors affecting surface roughness as well as the determination of optimal cutting combination by seeking the best surface roughness (response) and signal-to-noise ratio.

An accurate hysteresis model is fundamental to well capture the non-linearity phenomena occurring in structural and non-structural elements in building structures, that are usually made of reinforced concrete or steel materials. In this sense, this paper aims to numerically estimate through simplified non-linear analyses, the cyclic response of a reinforced concrete frame using different hysteretic models present in the literature. A commercial Finite Element Method package is used to carry out most of the simulations using polygonal hysteretic models and a fiber model, and additionally, a MATLAB script is developed to use a smooth hysteresis model. The experimental data is based on the experiments carried out in the Laboratório Nacional de Engenharia Civil, Portugal. The numerical outcomes are further compared with the experimental result to evaluate the accuracy of the simplified analysis based on the lumped plasticity or plastic hinge method for the reinforced concrete bare frame. Results show that the tetralinear Takeda’s model fits closely the experimental hysteresis loops. The fiber model can well capture the hysteresis behavior, though it requires knowledge and expertise on parameter calibration. Sivaselvan and Reinhorn’s smooth hysteresis model was able to satisfactorily reproduce the actual non-linear cyclic behavior of the RC frame structure in a global way.

The paper deals with chiral separation by simulated moving bed (SMB) chromatography. The separation of chiral epoxide enantiomers in microcrystalline cellulose triacetate using methanol as eluent is considered as illustrative example. The use of microcrystalline cellulose triacetate for the chromatographic separation of enantiomers is first reviewed and a methodology for obtaining basic data (adsorption equilibrium isotherms, axial dispersion and mass transfer coefficient) is discussed. A model for the prediction of the cyclic steady-state performance of the SMB, based on the analogy with the true moving bed, is developed assuming multicomponent adsorption equilibria, axial dispersion flow and linear driving force approximation to describe the intraparticle mass transfer rate. The simulation package is used to predict the effect of operating variables on the process performance and to define the regions for enantiomer separation. A simple optimization procedure is proposed for choosing the best SMB operating conditions. This procedure is extensively tested for the separation of chiral epoxide enantiomers. The experimental operation of a SMB pilot unit was carried out for this system. Purities and recoveries higher than 90% were obtained for both extract and raffinate, using a 420 ml inventory of stationary phase. The SMB pilot allows the continuous resolution of 52 g of racemic mixture per day and per liter of bed, with a solvent consumption of 0.4 l of mobile phase per gram of racemic mixture processed. The simulation package is also used to predict the steady-state internal concentration profiles for the SMB operation with reasonable agreement with experimental results.

This work intends to valorise by-products of the industrial processing of mushrooms to obtain ergosterol as a value-added compound. Agaricus bisporus L. is the world’s most consumed mushroom and one of the richest sources of ergosterol. Microwave-assisted extraction was used to replace conventional techniques that are time-consuming and need large amounts of solvent. Time (3-20 min), temperature (60-210 ºC) and solid-liquid ratio (1-20 g/L) were found the relevant variables to analyze the extraction process. To maximize the ergosterol extraction yield, response surface methodology was used to optimize the process. The global optimal extraction conditions were determined and comprise: 19.4±2.9 min, 132.8±12.4 ºC and 1.6±0.5 g/L, yielding 556.1±26.2 mg of ergosterol per 100 g of mushroom by-products. In the MAE optimal conditions, it was possible to obtain ergosterol in a similar value to the one obtained in other works when using the Soxhlet extraction method with a significant decrease in the time of extraction. The results show the potential of using the by-products of an agroindustry, mushrooms processing industry, as productive sources of ergosterol.

Forests provide many goods and services supported by different functions and involving different stakeholders. For example, the actual social context favours recreational uses of forests but, at the same time, forest managers and owners aim the highest production of wood products. It means that depending on the point of view of person that is seeing the forest, this has different value. Or, in other words, the value of forest is subjective depending on the demand of services and productions. In this case the human factor appears, and psychology should be taken into account in the decision‐making process. In thes terms, expert opinion is characterized by a large experience in the forest sector and can provide enough information to evaluate suitability of forests according to different objectives, products, services, and uses. In this context, the forest management has to integrate many disciplines and the trend in decision making is to use a multi‐actor approach, overall in mountains region, where several ecosystem services and products could be necessary evaluated in decision making to identify conflicts among uses/objectives. The combination of: i) expert opinion, by analysis judgments methods like Analytic Hierarchy Process (AHP) or Multi‐Attribute Utility Theory (MAUT), ii) growth forest dynamic models, iii) different spatial information and iv) Multi‐criteria Decision Making methods like linear programming (LP), we have the capability of simulate different and complex scenarios in temporal and spatial scales. This combination of methods is not easy because require of the use of too large data and calculation process. To solve it we developed AppTitude® to automatize the hierarchy of methods to evaluate the suitability of the Nordeste region for different forest management objectives and will compare it with the actual distribution forests and their uses/objectives.

Biomass is widely recognized as one of the main potential sources for renewable and sustainable generation of fuels, chemicals and other carbon-based materials for a long time. According to International Energy Agency, biomass energy accounts for about 14% of the world's total primary energy supply in 2017. There are many advantages in using biomass as an energy source, namely its carbon neutrality and being a non-polluting energy source. Many processes can be used to obtain fuels and chemicals from biomass, and the pyrolysis process is a renewable, economical, and efficient way to produce energy [1]. Pyrolysis is one of the main technologies for biomass conversion into energy. It consists of a thermal decomposition process in an inert atmosphere with absence of oxygen, to convert biomass into biochar (solid fraction), bio-oil (liquid fraction) and gases. Pyrolysis is a recognized industrial process for biomass conversion. No waste is generated in the process, as the bio-oil and biochar can each be used as a fuel and as fertilizer respectively, and the gases can be recycled back into the process [2]. A biomass sample (pellets) was characterized by proximate analysis, determining the fixed carbon (F.C.), moisture, volatiles and ashes composition, and by ultimate analyses, determining the content of C, H, N, S and O. The content of hemicellulose, lignin and cellulose was also determined. The methodologies are described elsewhere [3]. All characterizations were performed on a dry basis, at the conditions in which the sample was previously dried. Pyrolysis tests were performed in a fixed-bed vertical pyrolysis oven, with a maximum temperature of 500 C, a heating rate of 10, 20, 35 and 40 C/min, a retention time of 0.5 h and an N2 flow of 20 mL/min. The bio-oil produced was qualitatively characterized using FTIR

Neste trabalho exploram-se alguns dos actuais recursos de computação científica no contexto da optimização estática e dinâmica. Começa-se por propor um conjunto de procedimentos computacionais algébricos que permitem automatizar todo o processo de obtenção de simetrias e leis de conservação, quer no contexto clássico do cálculo das variações, quer no contexto mais abrangente do controlo óptimo. A utilidade do package de funções desenvolvido é demonstrada com a identificação de novas leis de conservação para alguns problemas do controlo óptimo conhecidos na literatura. Estabelece-se depois uma relação entre as simetrias variacionais do controlo óptimo e as simetrias de equações diferenciais ordinárias. A partir dessa relação, deduz-se um método construtivo, alternativo aos já existentes, para obtenção de simetrias nesta segunda classe de problemas. Numa segunda parte do trabalho, investigam-se, com recurso a simulações computacionais, formas de corpos não convexos que maximizem a sua resistência aerodinâmica quando se desloquem em meios rarefeitos e, simultaneamente, exibam um ligeiro movimento rotacional. É obtido um importante resultado original para o caso bidimensional. Trata-se de uma forma geométrica que confere ao corpo uma resistência muito próxima do seu limite teórico (R=1.4965<1.5). In this thesis some of the scientific computational resources are explored in the context of static and dynamic optimization. A set of analytical computational tools is proposed in order to allow the identification, in an automatic way, of variational symmetries and conservation laws in the calculus of variations and optimal control. The usefulness of the developed routines is showed with the identification of new conservation laws to concrete optimal control problems found in the literature. A relationship between the variational symmetries of optimal control and the symmetries of ordinary differential equations is established. Based in this relationship, a constructive method is created for the purpose of getting the symmetries in this second class of problems. Finally, we investigate, by means of computational simulations, shapes of nonconvex bodies that maximize resistance to its motion on a rarefied medium, considering that bodies are moving forward and at the same time slowly rotating. An important result is obtained for the two-dimensional case which consists of a geometric shape that confers to the body a resistance very close to the supremum value (R = 1.4965 < 1.5). Some results of the thesis are available in the English language in the following references: the research reports [29, 35, 37, 79], the poster [36], the conference proceedings with referee [34] and the refereed journals [31, 32, 38, 80].

This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.