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The study of the operation of quarry vehicles made it possible to form the target function of the study, taking into account the criterion of the efficiency of all processes of the system, which provides for the reduction of costs for the operation of the transport and production system of the quarry of a metallurgical enterprise, namely, the subsystems: “Incoming raw materials”, “Processing of raw materials”, “Sales of raw materials”. Factors influencing the cost indicator are highlighted. These factors include the production downtime of motor vehicles, the speed of motor vehicles with cargo, and the speed of motor vehicles without cargo. The values of these factors were obtained in result of timing the operation of motor vehicles on technological routes for four days. The levels of variation intervals and the nature of their changes for the three regimes were calculated for each of the subsystems. A regression analysis of the investigated factors was carried out to model the costs. The response surfaces of the obtained mathematical models are constructed, namely: the influence of the production downtime of motor vehicles and the speed of movement without cargo on the costs of functioning of subsystems, the influence of production downtime of motor vehicles and the speed of movement with cargo on the costs of the functioning of subsystems, the influence of the speed of movement with cargo and speed of movement without cargo for the costs of functioning of subsystems. The optimal values for reducing the cost of functioning of the “Processing of raw materials” subsystem are the value of production downtime – 4-5 min., the speed of motor vehicles without cargo – 9 min., and the speed of motor vehicles with cargo – 9 km/h. The optimal values for reducing the cost of functioning of the “Sales of raw materials” subsystem are the value of production downtime – 4-6 min., the speed of motor vehicles without cargo – 14-16 min., and the speed of motor vehicles with cargo – 13-15 km/h. The optimal values for reducing the cost of functioning of the subsystem “Incoming raw materials” are: the value of production downtime is 4-5 minutes, the speed of motor vehicles without cargo is 7-8 km/h, the speed of motor vehicles with cargo is 10 km/h.

This work aims to define the influence of different parameters (such as ethanol concentration, type (regular or homologation fuel), and formulation (olefin and aromatic contents) of gasoline, temperature, and aging duration) in gum formation in Brazilian ethanol–gasoline blends. As a result, a database with more than 500 cases was built gathering experimental measures of unwashed and washed gum contents from the literature and original experimental data. Two approaches considered to define the mathematical models capable of predicting gum formation are compared: a linear equation with rectangular interaction terms and artificial neural network (ANN) models. Different ANN topologies were investigated and the most robust models were compared to polynomial equations. Then, response surfaces were plotted to verify the consistency of the model in the entire experimental domain. The ANN models performed better. Indeed, the coefficient of determination reached values as high as 0.953 and 0.984, for the testing data of washed and unwashed gum content, respectively, and lower differences with experimental data were observed, up to 0.5 and 0.2%, respectively. Additionally, the ANN models were more robust than the specific quadratic model available in the literature. In terms of the impact of the ethanol, it is possible to confirm a catalytic effect after aging at medium or high temperatures for washed gum formation at low concentrations of ethanol in gasoline. Without aging or after storage at low temperatures, ethanol has a simple dilution effect. Such conclusions are in agreement with previous literature and explain why some authors observed either catalytic or dilution effects of ethanol. [ABSTRACT FROM AUTHOR]

To optimize the composition of steel in the work aims to identify the minimum required number of chemical elements and their content to avoid overheating and unnecessary economic costs. The solution is proposed in the form of microalloying, which leads to grinding of austenitic grains, and after phase transformation and ferritic grains, which improves the properties of steel. The established regularities allowed to quantitatively substantiate the chosen optimal chemical composition of steel, namely to establish the limit of variation of microalloying elements - chromium, vanadium and titanium. It was found that the obtained steel in comparison with the prototype has higher values of impact strength, while maintaining high strength and fluidity. Thus, the combination of improved service and mechanical properties of steel helps to increase the service life of its products. [ABSTRACT FROM AUTHOR]

The effect of Metakaolin and ground Scoria on various properties of concrete were investigated and optimised using Response Surfaces Methodology (RSM) in this study. Seven batches of concretes were cast at water to cement ratio of 0.5 and 5% fixed Metakaolin with 0, 5, 10, 15 and 20% Ground Scoria replaced cement. The resulting concrete then was tested for Slump. The Concrete cubes were cast and cured for 3, 7, 14, 28 and 60days before Water absorption and Compressive strength test were carried out. at all replacement levels of Metakaolin/Scoria content, Workability, Water absorption, Density and Compressive strength decreased when compared with the control concrete. However, Water absorption, Density and Compressive strength slightly increased with increase in curing age. The models developed were quite accurate as the percentages of error were in a good agreement and can explain the variability in Metakaolin/Ground Scoria concrete. Numerical method of optimisation was applied to determine the optimum mix proportions for Metakaolin/Ground Scoria. The optimum mix of concrete was obtained by addition of 8.60% GS after curing for 12-days with 0.8 desirability. Based on the result of optimisation, incorporating the optimum values of 13.6% (5%MK+8.6%GS) metakaolin and ground Scoria in concrete as cement substitute for every one kilogram of cement, can potentially result in reduction of CO2 emissions by 0.07-0.1224kg. [ABSTRACT FROM AUTHOR]

THE PAPER PRESENTS THE FORMULATION OF A GRANULAR COMPUTATIONAL HOMOGENISATION PROBLEM and the proposition of a method to solve it, which enables multiscale analysis of materials with uncertain microstructure parameters. The material parameters and the geometry, represented by the interval and fuzzy numbers, are assumed to be unprecise. An α-cut representation of fuzzy numbers allows the use of interval arithmetic for epistemic uncertainties. Directed interval arithmetic is used to reduce the effect of interval widening during arithmetic operations. Response surfaces of diverse types, including Artificial Neural Networks, are used as model reduction methods. The finite element method is employed to solve the boundary value problem on a micro scale. Numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Wood has gained popularity as a building and decorative material due to its environmentally friendly and sustainable characteristics. Yet, its long maturation time poses a limitation on meeting the growing demand for wood products. This challenge has led to the plantation of fast-growing wood as an alternative solution. Unfortunately, the poor mechanical properties of fast-growing wood hinder its application. In this study, we developed novel densification-modified wood by combining alkali chemical pretreatment, cyclic impregnation, and mechanical hot-pressing techniques. Additionally, the response surface method was employed to rapidly determine the optimal preparation parameters, reducing the cost of preparation under various conditions. The optimized parameters resulted in densification-modified wood with a flexural strength and modulus of elasticity of 337.04 MPa and 27.43 GPa, respectively. Furthermore, the densified wood achieved excellent dimensional stability by reducing the water-absorbing thickness swelling to 1.15 % for 72-h water soaking. The findings indicated that the densification-modified wood possessed high tensile strength and elastic modulus, along with excellent dimensional stability. The proposed densified wood modification technology in this study offers new perspectives and design guidance for the application of outdoor engineering structures, energy-efficient buildings, and decorative materials. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this work is to obtain a statistical mathematical model of cement-ash stone with the response function – the strength of the stone. In particular, the task is to establish the effect on the strength of cement stone additives in the form of ash-removal of thermal power plants (pozzolanic additive). Methodology. A rotatable central composition plan of the experiment was used to obtain a statistical model. The results were processed using the method of computer mathematical modeling using the system STATGRAPHICS Plus for Windows. Originality. It is established that the hypersurface G (X₁, X₂) has an extremum point, which allows to optimize the composition of the cement mixture by factors X₁, X₂. Practical value. The results obtained in this work make it possible to predict the strength characteristics of the stone on the basis of cement-ash composition. Finding. The statistical mathematical model of cement-ash stone on the basis of cement of general construction purpose of the ПЦI-500-Н brand is received. From the obtained pareto-graphs, hypersurfaces and contour curves, the nature and degree of influence of each of the polynomial members on the objective function – the strength of cement stone G (X₁, X₂, X₃) are determined. The factors of model X₁, X₃ have the greatest statistical significance, the factor X₂ is statically the least significant. The simulation results confirm that the water-mixture ratio does not have a significant effect on the strength of cement stone in a given range of formulations. The maximum strength of cement stone G = 13,582 MPa takes place at the optimum point with coordinates: Х₁ = 0,689; Х₂ = −0,295; Х₃ = 1,105.

Applying gel-type solid chlorine dioxide for the sustained release of chlorine dioxide has several shortcomings, such as no resistance to acid and alkali corrosion and poor mechanical properties. However, introducing quaternary ammonium, carboxyl, and amino groups into the hydrogel system can enhance its acid and alkali resistance. In this study, the effects of concentration of dry heat-modified starch, quaternized carboxymethyl cellulose, and chitin on the swelling behavior and mechanical properties of starch-based acid- and alkali-resistant hydrogels are investigated. The feasibility of the actual and predicted values of the tentative results is verified based on the response surface design to determine the optimal concentration ratio of acid- and alkali-resistant hydrogels. The results reveal that optimized process parameters are reliable. The maximum swelling ratio and compressive stress of the hydrogel are 5358.00% and 44.45 kPa, respectively, and its swelling behavior conforms to the pseudo second-order kinetic model. Thus, the present study can provide a new method of developing efficient starch-based chlorine dioxide hydrogels for the sustained release of chlorine dioxide. [ABSTRACT FROM AUTHOR]

Given the growing demand for water in the world, approaches and/or actions are needed to reduce consumption and increase demand. In the latter case, water reuse is a viable alternative to increase the demand for non-potable water in homes. This study aims to optimize washing machine greywater treatment using aqueous solutions of aluminum sulfate and Moringa oleifera seed extract. To this end, an experiment was carried out using washing machine greywater from a residence. The data from this test were evaluated adopting the Response Surface Methodology (RSM). The parameters evaluated in the experiment were the pH, turbidity, sludge volume produced, and the total dissolved solids. Afterward, these were compared with limits established by technical standards and authors regarding non-potable water reuse. The results of this study indicate that using the treatment with the lowest coagulant dosages, i.e., 20 mL L−1 of Moringa oleifera extracted in calcium nitrate and 1 mL L−1 of aluminum sulfate, it is feasible to reuse greywater after treatment in toilet bowl flushing. For these dosages of coagulants, the turbidity removal was 96.22% and the pH varied from 7.2 to 6.8. The association of aluminum sulfate with Moringa oleifera extracted in calcium nitrate is viable for the treatment by coagulation of greywater from the washing machine to reduce the dosage of each coagulant and meet the criteria of the standards.

Understanding the mechanical properties of membrane materials in two-dimensional (2D) stress space is critical for structural design and mechanical analysis of membrane structures. In this paper, an experimental study of the warp-knitted fabric PVDF8028 subjected to biaxial loads was performed to expose the detailed mechanical behaviors and determine proper elastic parameters for the fabrics under multiple stress ratios. The least-square method was adopted to calculate the elastic parameters for different stress states, and response surfaces of strain and elastic parameters were used to reveal the mechanical behaviors in detail. Comparison between coated plain-woven and warp-knitted fabrics was used for exhibiting the influences of microstructures and deformation mechanisms on the macroscopic mechanical properties of materials. The results show that the stress–strain behaviors exhibit significant nonlinearities, and could be characterized by appropriate response surfaces. The elastic stiffness response surfaces of loading and unloading processes could form an unbalanced X-shaped cross, and detailed elastic parameters in those two processes could be obtained by corresponding response surfaces. Compared with plain-woven fabrics, warp-knitted fabrics could exhibit more obvious nonlinear characteristics due to the existence of their coiled yarns and lower Poisson's ratios because of the special noncrimp yarn structure. The differences in macroscopic mechanical properties for these two materials result from the corresponding differences in microstructures and deformation mechanisms. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of this work is to obtain a statistical mathematical model of cement-ash stone with the response function - the strength of the stone. In particular, the task is to establish the effect on the strength of cement stone additives in the form of ash-removal of thermal power plants (pozzolanic additive). Methodology. A rotatable central composition plan of the experiment was used to obtain a statistical model. The results were processed using the method of computer mathematical modeling using the system STATGRAPHICS Plus for Windows. Originality. It is established that the hypersurface G (X1, X2) has an extremum point, which allows to optimize the composition of the cement mixture by factors X1, X2. Practical value. The results obtained in this work make it possible to predict the strength characteristics of the stone on the basis of cement-ash composition. Finding. The statistical mathematical model of cement-ash stone on the basis of cement of general construction purpose of the ПЦI-500-Н brand is received. From the obtained pareto-graphs, hypersurfaces and contour curves, the nature and degree of influence of each of the polynomial members on the objective function - the strength of cement stone G (X1, X2, X3) are determined. The factors of model X1, X3 have the greatest statistical significance, the factor X2 is statically the least significant. The simulation results confirm that the water-mixture ratio does not have a significant effect on the strength of cement stone in a given range of formulations. The maximum strength of cement stone G = 13,582 MPa takes place at the optimum point with coordinates: Х1 = 0,689; Х2 = -0,295; Х3 = 1,105. [ABSTRACT FROM AUTHOR]

Experiments on shrinkage reduction of high‐performance concrete components by heat treatment The paper investigates the shrinkage reduction of high‐performance concrete by heat treatment. Concretes are heat treated to accelerate hardening and to allow early stripping. Damage to the concrete structure is to be minimized by a suitable choice of maximum treatment temperature, heating and cooling rate. This leads to temperature durations of more than 24 h, which is not suitable for a serial production of precast concrete parts. For a minimally short heat treatment, a pre‐storage time and a regulated heating rate are omitted so that the heating starts directly after concreting. This shortening of the temperature duration has an influence on the shrinkage behavior. It is investigated here on prisms with and without steel fibers, as well as on beams with rebar ratios of 1.8 % and 3.1 %. All specimens are heat treated at 80 °C and 60 % relative humidity for 1, 2, 4, 6, and 24 h. Specimens without heat treatment serve as reference. The results show that the shrinkage strain is significantly reduced with increasing temperature duration. It also decreases with an increasing rebar ratio. In order to predict the shrinkage behavior as a function of temperature duration, data‐based response surfaces are derived and validated. Thereby, the prediction quality increases with increasing concrete age. [ABSTRACT FROM AUTHOR]

Applying gel-type solid chlorine dioxide for the sustained release of chlorine dioxide has several shortcomings, such as no resistance to acid and alkali corrosion and poor mechanical properties. However, introducing quaternary ammonium, carboxyl, and amino groups into the hydrogel system can enhance its acid and alkali resistance. In this study, the effects of concentration of dry heat-modified starch, quaternized carboxymethyl cellulose, and chitin on the swelling behavior and mechanical properties of starch-based acid- and alkali-resistant hydrogels are investigated. The feasibility of the actual and predicted values of the tentative results is verified based on the response surface design to determine the optimal concentration ratio of acid- and alkali-resistant hydrogels. The results reveal that optimized process parameters are reliable. The maximum swelling ratio and compressive stress of the hydrogel are 5358.00% and 44.45 kPa, respectively, and its swelling behavior conforms to the pseudo second-order kinetic model. Thus, the present study can provide a new method of developing efficient starch-based chlorine dioxide hydrogels for the sustained release of chlorine dioxide.

Abstract A parameterized dynamics analysis model of legged lander with adaptive landing gear was established. Based on the analysis model, the landing performances under various landing conditions were analyzed by the optimized Latin hypercube experimental design method. In order to improve the landing performances, a hierarchical optimization method was proposed considering the uncertainty of landing conditions. The optimization problem was divided into a higher level (hereafter the “leader”) and several lower levels (hereafter the “follower”). The followers took conditioning factors as design variables to find out the worst landing conditions, while the leader took buffer parameters as design variables to better the landing performance under worst conditions. First of all, sensitivity analysis of landing conditioning factors was carried out according to the results of experimental design. After the sensitive factors were screened out, the response surface models were established to reflect the complicated relationships between sensitive conditioning factors, buffer parameters and landing performance indexes. Finally, the response surface model was used for hierarchical optimization iteration to improve the computational efficiency. After selecting the optimum buffer parameters from the solution set, the dynamic model with the optimum parameters was simulated again under the same landing conditions as the simulation before. After optimization, nozzle performance against damage is improved by 5.24%, the acceleration overload is reduced by 5.74%, and the primary strut improves its performance by 21.10%.

Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also correlations between the parameters themselves. The interrelationship is not always evident even for someone skilled in the art. Therefore, optimisation using response surface can give more insights in the interconnections between parameters, but also between the material properties and the variables. Performing inspections using an optimised parameter set will result in high contrast thermograms showing the size and shape of the defect accurately. Using response surfaces to predict the optimal parameter set enables to perform fast measurements without the need of extensive testing to find adequate measurement parameters. Differing from the optimal parameters will result in contrast loss or detail loss of the size and shape of the detected defect. [ABSTRACT FROM AUTHOR]

Two optimization problems are formulated to improve the effectiveness and productivity of pultrusion processes, to preserve the quality of pultruded profiles, and to take into account the ambient industrial shop temperature and requirements of process technologists. To solve these problems, an optimization methodology using designed computer experiments and the response surface technique was developed. The effects of room temperature and curing allowed behind the die exit on the energy consumption and pull speed were investigated. A more accurate and realistic process optimization was achieved by the temperature control strategy with heater switch-on and -off operations. This indirect optimization methodology allowed us to develop interactive technological maps on the basis of an accessible-to-all Excel code for technologists working in industrial shops. As an example, demonstrating the effectiveness of the methodology developed and utilization of the interactive technological map, the optimization of a real pultrusion process, producing two rod profiles with ears simultaneously, is carried out. [ABSTRACT FROM AUTHOR]

Pre-cooked adzuki beans (Vigna angularis), which looks like dried adzuki bean, is easily cooked and preserved. This study aimed to optimize the microwave pre-cooked conditions on adzuki beans by applying the response surface methodology. The results showed that soaking time has a significant effect on the gelatinization degree of adzuki beans according to microwave time. The most suitable gelatinization and the sensory scores were obtained with a soaking time of 7.8 h, a microwave power of 830 W, and microwave time of 92 s. The pre-cooked treatment had no significant effect (p > 0.05) on the protein, free amino acid, fat and starch content of adzuki bean products. The results of SEM and polarized light microscopy showed that the surface and center of starch were damaged after microwave treatment. XRD showed that microwave pre-cooking did not change the crystal structure of starch and maintained the original order of type A structure while reducing the relative starch crystallinity. FT-IR showed that the pre-cooked treatment did not produce new structure in adzuki bean starch, but the ratio of 1047/1022 cm−1 was slightly decreased, indicating that the starch crystallization area decreased relative to the amorphous area and the relative crystallinity decreased. The results of FTIR were consistent with X-ray diffraction results. Therefore, microwaves improved the gelatinization of adzuki beans and made the pre-cooked adzuki beans more suitable.

This work studied the transesterification reaction of palm kernel oil to produce Biodiesel FAME, using as catalyst KOH incorporated as a potassium methoxide intermediate. The catalytic tests were performed modifying representative variables such as reaction temperature (°C), methanol/oil molar ratio, and catalyst content (%KOH). The experimental data were adjusted to a linear empirical model, finding that the best experimental condition was observed at 50 °C with a methanol/oil ratio of 5.5 and a% KOH of 0.8. Finally, the FAME was characterized by FTIR spectroscopy, gas chromatography, and ASTM quality control techniques for analysis of cold properties, transport properties, and combustion properties. The reaction rate was determined and a reaction mechanism was proposed based on the experimental results. [ABSTRACT FROM AUTHOR]

• Cleaning oil-based paintings is a complex, delicate matter. • A statistical model is proposed to study response surfaces using MODDE GO®. • Cleaning formulas are tested that contain water, limonene, Findet® 1214/N23, phenethyl alcohol and Glucopon® 600. • Different factors are analyzed: chemical composition, expert opinion, lightness and gloss. • Diverse cleaning conditions can be simulated and the main factors that influence the process calculated. In order to clean varnishes used as final protective layers in paintings and polychromed sculptures,organic solvents often associated with high toxicity, have traditionally been used that pose a risk to the original materials. Therefore, it is necessary to develop cleaning methods that use substances that are as innocuous as possible for the artwork and the restorers themselves. Water-based cleaning systems have been put forward in recent years, usually accompanied by surfactants, which under certain circumstances can provide significant benefits. We are presenting this work along these lines, proposing a model for studying response surfaces that analyses the performance in cleaning and removing varnish from artworks using various cleaning formulas comprised mainly of water and a low-toxicity monoterpene: limonene. Their level of effectiveness has been evaluated using the software for statistical design and optimization of experiments, MODDE GO®. The study shows the model's statistical validity and its ability to simulate a multitude of cleaning scenarios in silico and to determine the main factors that affect the cleaning, evaluated via the responses according to: chemical composition, expert opinion using visible light, expert opinion using ultraviolet light, variation in color and percentage variation of lightness and gloss. [ABSTRACT FROM AUTHOR]

The necessary composition and content of fillers of a multicomponent extrudable composition based on an ultrahigh-molecular polyethylene (UHMPE) with specified mechanical and tribotechnical characteristics are determined. For the purpose of effective and operational development of extrudable composites based on an UHMPE, a minimum amount of experimental data and an algorithm for determining the governing parameters (formulations) is used, which give the needed (restrictive) values for the effective characteristics of multicomponent polymer-polymer compositions. This allows one to secure the tribological, mechanical and technological properties required. The limited amount of experimental data for the relation between the effective properties and the formulation required is supplemented by values calculated using the Lagrange interpolation polynomial. The relations obtained are represented as surfaces and the corresponding level lines. The imposition of level lines allows one to determine the area that meets the requirements specified and to find the formulation necessary for it. On the basis of this algorithm, the optimum formulation of a three-component mixture consisting of an UHMPE, a high-density polyethylene grafted with maleic anhydride, and a polypropylene having tribomechanical properties at the level of an UHMPE, but with a melt flow-behavior index suitable for 3D printing by fused deposition modeling, is determined. According to the formulation found, samples were made and tested, and they completely satisfied the properties prescribed. [ABSTRACT FROM AUTHOR]

Metamodelling or surrogate modelling techniques are frequently used across the engineering disciplines in conjunction with expensive simulation models or physical experiments. With the proliferation of metamodeling techniques developed to provide enhanced performance for specific problems, and the wide availability of a diverse choice of tools in engineering software packages, the engineering task of selecting a robust metamodeling technique for practical problems is still a challenge. This research introduces a framework for describing the typology of engineering problems, in terms of dimensionality and complexity, and the modelling conditions, reflecting the noisiness of the signals and the affordability of sample sizes, and on this basis presents a systematic evaluation of the performance of frequently used metamodeling techniques. A set of metamodeling techniques, selected based on their reported use for engineering problems (i.e. Polynomial, Radial Basis Function, and Kriging), were systematically evaluated in terms of accuracy and robustness against a carefully assembled set of 18 test functions covering different types of problems, sampling conditions and noise conditions. A set of four real-world engineering case studies covering both computer simulation and physical experiments were also analysed as validation tests for the proposed guidelines. The main conclusions drawn from the study are that Kriging model with Matérn 5/2 correlation function performs consistently well across different problem types with smooth (i.e. not noisy) data, while Kriging model with Matérn 3/2 correlation function provides robust performance under noisy conditions, except for the very high noise conditions, where the Kriging model with nugget appears to provide better models. These results provide engineering practitioners with a guide for the choice of a metamodeling technique for problem types and modelling conditions represented in the study, whereas the evaluation framework and benchmarking problems set will be useful for researchers conducting similar studies. [ABSTRACT FROM AUTHOR]

As systems grow more complex, so does our propensity to use computers to emulate complex real-world systems. Often these real-world systems possess dynamic response behavior over the operational domain of input parameter configurations. This domain is referred to as the design space or experimental region. It is critical we ensure that computer models which emulate such dynamic behavior be validated over the full design space. This paper presents a dual-interval validation methodology. Confidence intervals and tolerance intervals are developed based on a system response surface function. Model samples are compared to each interval to develop a complete model validation conclusion. The methodology is described, its robustness to noise and model lack-of-fit examined, and then it is applied to a well-established engineering validation challenge problem. [ABSTRACT FROM AUTHOR]

Many complex processes have a low degree of automation, and oftentimes important quality information is only available hours or even days after the production is completed. This article shows how multivariate design and response surface modeling were applied to a lay-flat plastic hose extrusion process in a full-scale experiment. Clear quantitative relationships were found, which to a large degree match existing qualitative process understanding. For instance, it was quantified how adhesion improves with increased extrusion screw speed and extrusion head temperature. The results can readily be used to inform the operators in real-time of important quality parameters of the hose currently under production. The clear results also indicate that increased process automation is achievable.

The efficiency of airplane turbines must be improved by optimizing the characteristics of the labyrinth seals and improving their operational efficiency. The response surface is plotted as a function of the geometric parameters for the labyrinth seals of gas-turbine engines. On that basis, an optimized labyrinth seal is developed. The characteristics of the initial and optimized seals are compared. [ABSTRACT FROM AUTHOR]

Nowadays, performing dynamic line scan thermography (DLST) is very challenging, and therefore an expert is needed in order to predict the optimal set-up parameters. The parameters are mostly dependent on the material properties of the object to be inspected, but there are also correlations between the parameters themselves. The interrelationship is not always evident even for someone skilled in the art. Therefore, optimisation using response surface can give more insights in the interconnections between parameters, but also between the material properties and the variables. Performing inspections using an optimised parameter set will result in high contrast thermograms showing the size and shape of the defect accurately. Using response surfaces to predict the optimal parameter set enables to perform fast measurements without the need of extensive testing to find adequate measurement parameters. Differing from the optimal parameters will result in contrast loss or detail loss of the size and shape of the detected defect.

Gas extraction in the Groningen Province in the Netherlands has caused seismicity. A method was needed for probabilistic assessments of the seismic performance of the levees that protect low-lying polders against flooding. By combining the First Order Reliability Method with response surfaces it proved possible to strongly reduce the required number of simulations with advanced numerical models to obtain reliable failure probability estimates. To illustrate the workings of the method, an application to a levee cross-section along the Eemscanal with a sheet pile wall is presented. The probabilistic method can be used for probabilistic assessments and the probability-based calibration of partial factors, and it could serve as a starting point for quantitative risk analyses for levee systems in earthquake prone regions. [ABSTRACT FROM AUTHOR]

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Characterizing the operation of a novel torrefaction plant based on horizontal moving bed reactors was the focus of this research work. Coppiced willow biomass was used as the feedstock for the torrefaction experiments carried out with this plant. The operating temperature and torrefaction residence time were each varied over a wide range in order to understand how these two parameters each influence torrefaction severity. The torrefaction severity was characterized by developing response surface plots for torrefaction metrics, including mass yield, energy yield, milling energy, and elemental composition. The operating conditions were varied from 220–280 °C with an 8–24 min residence time. The coppiced willow biomass was processed into a granular form, resulting in an average length and diameter of 19.3 and 7.5 mm, respectively. The operating conditions were found to produce a wide range of torrefaction severities with a mass yield range from 64.7–91.6 %. Temperature was found to have a greater influence over severity than exposure time. In order to understand the uncertainty of the measurements, experiments were repeated at a central point (250 °C and 16 min) six times. The repeatability was found to be ±0.72 % at the 95 % confidence limit. The response surfaces for mass and energy yield indicate that, for this feedstock, torrefaction severity below 80 % mass yield can be achieved in under 10 min at temperatures of 255 °C and above. This performance compares very favourably with other continuously fed pilot scale torrefaction plants. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present the model‐based optimization results of an anaerobic granular sludge internal circulation reactor. The International Water Association Anaerobic Digestion Model No. 1 extended with phosphorus (P), sulfur (S), and ethanol is used to describe the main biological and physico‐chemical processes. The high‐rate conditions within the reactor are simulated using a flow + reactor model comprised of a series of continuous stirred tank reactors followed by an ideal total suspended solids separation unit. Following parameter estimation by least squares on the measured data, the model had a relative mean error of 13 and 15% for data set #1 and data set #2, respectively. Response surfaces show that the reactor performance index (a metric combining energy recovery in the form of heat and electricity, as well as chemicals needed for pH control) could be improved by 45% when reactor pH is reduced down to 6.8. Model‐based results reveal that influent S does not impose sufficient negative impacts on energy recovery (+5.7%, in MWh/day,+0.20 M€/year when influent S is removed) to warrant the cost of its removal (3.58 M€/year). In fact, the process could handle even higher S loads (ensuring the same degree of conversion) as long as the pH is maintained above 6.8. Nevertheless, a higher S load substantially increases the amount of added NaOH to maintain the desired operational pH (>25%) due to the acidic behavior of HS −. CO 2 stripping decreases the buffer capacity of the system and hence use of chemicals for pH control. Finally, the paper discusses the possibilities and limitations of the proposed approach, and how the results of this study will be put into practice. The objective of this paper is to present the model‐based optimization results of an anaerobic granular sludge internal circulation reactor. [ABSTRACT FROM AUTHOR]

Paper presents kinetic models of changes in genetic controlling systems of cells in the states of cell proliferation and differentiation. It was shown that the changes in the genetic control of cells in the state of proliferation and differentiation occur at the maximal rate of reactions constants. It was built on response surfaces for each of the reaction rate constants showing what parameters of the model make the largest contribution to the value of each of them. It was established that the greatest contribution to the rate of reaction constants of changes in the genetic controlling systems of cells in the state of proliferation have histone genes and cyclin-dependent kinases, and a little less – genes-stimulators of proliferation and transcription factors. In cells in diffe­rentiation a state of inhibitors of cyclin-dependent kinases, and equally transcription factors, cell cycle genes, transcription proteins genes, structural genes and hyperpolarization of the cell membrane. As a result, we got data that the value concentration of cyclin-dependent kinases and inhibitors of cyclin-dependent kinases in the cell is the trigger that determines whether a cell proliferates or differentiates. Also, it was set specific numerical value of each of the reactions rate constants which characterize changes in the genetic control of cell in the state of proliferation and differentiation.

Graphical abstract Highlights • A novel model-based parameter tuning strategy for tuning under restricted budgets is proposed. • A new paradigm for tuning is proposed that only requires candidate parameter vectors to be sampled once. • This is achieved by evaluating a newly proposed Statistical Meta-Optimisation Fitness (SMOF) measure. Abstract Parameter tuning aims to find suitable parameter values for heuristic optimisation algorithms that allows for the practical application of such algorithms. Conventional tuning approaches view the tuning problem as two distinct problems, namely, a stochastic problem to quantify the performance of a parameter vector and a deterministic problem for finding improved parameter vectors in the meta-design space. A direct consequence of this viewpoint is that parameter vectors are sampled multiple times to resolve their respective performance uncertainties. In this study we share an alternative viewpoint, which is to consider the tuning problem as a single stochastic problem for which both the spatial location and performance of the optimal parameter vector are uncertain. A direct implication, of this alternative stance, is that every parameter vector is sampled only once. In our proposed approach, the spatial and performance uncertainties of the optimal parameter vector are resolved by the spatial clustering of candidate parameter vectors in the meta-design space. In a series of numerical experiments, considering 16 test problems, we show that our approach, Efficient Sequential Parameter Optimisation (ESPO), outperforms both F/Race and Sequential Parameter Optimisation (SPO), especially for tuning under restricted budgets. [ABSTRACT FROM AUTHOR]

Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle this problem several algorithms have been developed using surrogates. However, these often have disadvantages such as the requirement of a priori knowledge of the output functions or exponentially scaling computational cost with respect to the number of objectives. In this paper a new algorithm is proposed, TSEMO, which uses Gaussian processes as surrogates. The Gaussian processes are sampled using spectral sampling techniques to make use of Thompson sampling in conjunction with the hypervolume quality indicator and NSGA-II to choose a new evaluation point at each iteration. The reference point required for the hypervolume calculation is estimated within TSEMO. Further, a simple extension was proposed to carry out batch-sequential design. TSEMO was compared to ParEGO, an expected hypervolume implementation, and NSGA-II on nine test problems with a budget of 150 function evaluations. Overall, TSEMO shows promising performance, while giving a simple algorithm without the requirement of a priori knowledge, reduced hypervolume calculations to approach linear scaling with respect to the number of objectives, the capacity to handle noise and lastly the ability for batch-sequential usage. [ABSTRACT FROM AUTHOR]

Surrogate models approximate a function based on a set of training points and can then predict the function at new points. In engineering, kriging is widely used because it is fast to train and is generally more accurate than other types of surrogate models. However, the prediction time of kriging increases with the size of the dataset, and the training can fail if the dataset is too large or poorly spaced, which limits the accuracy that is attainable. We develop a new surrogate modeling technique—regularized minimal-energy tensor-product splines (RMTS)—that is not susceptible to training failure, and whose prediction time does not increase with the number of training points. The improved scalability with the number of training points is due to the use of tensor-product splines, where energy minimization is used to handle under-constrained problems in which there are more spline coefficients than training points. RMTS scales up to four dimensions with 10–15 spline coefficients per dimension, but scaling beyond that requires coarsening of the spline in some of the dimensions because of the computational cost of the energy minimization step. Benchmarking using a suite of one- to four-dimensional problems shows that while kriging is the most accurate option for a small number of training points, RMTS is the best alternative when a large set of data points is available or a low prediction time is desired. The best-case average root-mean-square error for the 4-D problems is close to 1% for RMTS and just under 10% for kriging. [ABSTRACT FROM AUTHOR]