Fuzzy logic model for the prediction of cement compressive strength

A fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO₃, and C₃S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.     

The use of GA-ANNs in the modelling of compressive strength of cement mortar

In this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C₃S, SO₃ and surface area led to increased strength within the limits of the model. C₂S decreased the strength whereas C₃A decreased or increased the strength depending on the SO₃ level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.     

Predicting the flux decline in milk cross-flow ceramic ultrafiltration by artificial neural networks

Fouling is complex phenomenon and an important drawback in the operation of membrane processes, thus its modeling involves scientific and commercial interest. In this research work, experimental data were collected by carrying out a sequence of cycles comprising both milk ultrafiltration through a 50 kDa tubular ceramic membrane and cleaning protocols with different agents. Then, it was developed an artificial neural network model that receives as inputs the operational cycle, the aggressivity of the cleaning and the filtration time and returns as output the permeate flux. Several training algorithms were tested and excellent fitting was obtained with the Levenberg-Marquardt one.     

Application of artificial neural networks to predict chemical desulfurization of Tabas coal

This paper presents a neural network model to predict the effects of operational parameters on the organic and inorganic sulfur removal from coal by sodium butoxide. The coal particle size, leaching temperature and time, sodium butoxide concentration and pre oxidation time by peroxyacetic acid (PAA) were used as inputs to the network. The outputs of the models were organic and inorganic sulfur reduction. Feed-forward artificial neural network with 5-7-10-1 arrangement, were capable to estimate organic and inorganic sulfur reduction, respectively. Simulated values obtained with neural network correspond closely to the experimental results. It was achieved quite satisfactory correlations of R² = 1 and 0.96 in training and testing stages for pyritic sulfur and R² = 1 and 0.97 in training and testing stages, respectively, for organic sulfur reduction prediction. The proposed neural network model accurately reproduces all the effects of operational variables and can be used in the simulation of Tabas coal desulfurization plant.     

The use of artificial neural networks in electrostatic force microscopy

ABSTRACT: The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.     

Improving compressive strength of fly ash-based geopolymer composites by basalt fibers addition

In this study, ASTM Class C fly ash used as an alumino-silicate source was activated by metal alkali and cured at low temperature. Basalt fibers which have excellent physical and mechanical properties were added to fly ash-based geopolymers for 10–30% solid content to act as a reinforced material, and its influence on the compressive strength of geopolymer composites has been investigated. XRD study of synthesized geopolymers showed an amorphous phase of geopolymeric gel in the 2θ region of 23°–38° including calcium-silicate-hydrate (C-S-H) phase, some crystalline phases of magnesioferrite, and un-reacted quartz. The microstructure investigation illustrated fly ash particles and basalt fibers were embedded in a dense alumino-silicate matrix, though there was some un-reacted phase occurred. The compressive strength of fly ash-based geopolymer matrix without basalt fibers added samples aged 28 days was 35 MPa which significantly increased 37% when the 10 wt%. basalt fibers were added. However, the addition of basalt fibers from 15 to 30 wt% has not shown a major improvement in compressive strength. In addition, it was found that the compressive strength was strong relevant to the Ca/Si ratio and the C-S-H phase in the geopolymer matrix as high compressive strength was found in the samples with high Ca/Si ratio. It is suggested that basalt fibers are one of the potential candidates as reinforcements for geopolymer composites development.     

Nonlinear system identification and model reduction using artificial neural networks

We present a technique for nonlinear system identification and model reduction using artificial neural networks (ANNs). The ANN is used to model plant input–output data, with the states of the model being represented by the outputs of an intermediate hidden layer of the ANN. Model reduction is achieved by applying a singular value decomposition (SVD)-based technique to the weight matrices of the ANN. The sequence of state values is used to convert the model to a form that is useful for state and parameter estimation. Examples of chemical systems (batch and continuous reactors and distillation columns) are presented to demonstrate the performance of the ANN-based system identification and model reduction technique.     

RP-HPLC optimization of econea by using artificial neural networks and its antifouling performance on the Turkish coastline

Coverage of artificial surfaces within seawater by fouling organisms is defined as biofouling. Although biofouling is a natural process, it has some disadvantages for shipping industry such as increased fuel consumption, and CO₂ emission. Therefore, the ships' hull must be covered by antifouling (AF) or fouling release type coatings to overcome biofouling. In general, the so-called self-polishing AF paints contain biocides for preventing fouling organisms. Their concentrations and release rates from AF coatings are of great importance and they definitely affect both quality and cost of the coating. In the present study, we aimed at applying a new robust method. In this method, we used a model biocide, i.e., econea, to obtain its RP-HPLC optimization through artificial neural networks (ANN) and to see its antifouling performance. Column temperature, mobile phase ratio, flow rate, concentration and wavelength as input parameters and retention time as an output parameter were used in the ANN modeling. In conclusion, the R&D groups in AF paint industry may use RP-HPLC method supported with ANN modeling in further studies.     

A mixed-integer programming approach for optimal configuration of artificial neural networks

A mathematical programming approach for automatic computation of the optimal configuration of artificial neural networks (ANNs) is presented. Training of the network is modelled as a mixed-integer program (MIP) where 0–1 binary variables are introduced to represent the existence (binary variable = 1) and non-existence (binary variable = 0) of the nodes and the interconnections between the nodes. The objective is to minimize the number of nodes and/or interconnections to meet a given error criteria. From modelling point of view, the key advantage of the proposed approach is that the user does not have to try different configurations of the network, a solution of the proposed MIP formulation automatically generates the optimal configuration of the network. From the implementation of ANN point of view, a simplified representation of the network is obtained, where redundant nodes and interconnections have been eliminated. A number of examples are presented to demonstrate the applicability of the proposed approach.     

Mechanical and microstructural characterization of geopolymers synthesized from FCC waste catalyst and silica fume

A FCC waste catalyst-based geopolymer was synthesized from FCC waste catalyst and silica fume, which were used as the main silicon-aluminum raw material and correction material, respectively. Meanwhile, NaOH and water glass composite were used as alkaline activator in the preparation process. Herein, the effects of silicon correction materials, alkaline activator modulus, and silica fume content on the compressive strength performance of prepared geopolymers were discussed. The microstructure was comprehensively analyzed by X-ray diffraction, fourier infrared spectroscopy, nuclear magnetic resonance spectroscopy and scanning electron microscope. The results showed that the prepared geopolymer has good early property when the silica fume content is 50% and the water glass modulus is 1.2. The 3d compressive strength of the obtained sample reaches 23.77 MPa. Microstructure and geopolymerization process analysis indicate that the FCC waste catalyst and silica fume have a good synergistic effect, which confirms the feasibility of preparing the geopolymer by using these industrial waste materials.     

Prediction of seasonal variation of butters by computing the fatty acids composition with artificial neural networks

The seasonal variation of the fatty acids composition of butters were investigated over three seasons during a 12‐month study in the protected designation of origin Parmigiano‐Reggiano cheese area. Fatty acids were analyzed by GC‐FID, and then computed by artificial neural networks (ANN). Compared with spring and winter, butter manufactured from summer milk creams showed an optimal saturated/un‐saturated fatty acids ratio (?8.89 and ?5.79%), lower levels of saturated fatty acids (?2.63 and ?1.68%) and higher levels of mono‐unsaturated (+5.50 and +3.45%), poly‐unsaturated fatty acids (+0.65 and +0.17%), and rumenic acid (+0.55 and +3.41%), while vaccenic acid had lower levels in spring and higher in winter (?2.94 and +2.91%). Moreover, the ANN models were able to predict the season of production of milk creams, and classify butters obtained from spring and summer milk creams on the basis of the type of feeding regimens. Practical applications: The investigation on variables that affect the milk fatty acids composition can improve the quality of milk across all systems, and the combination of chromatographic and computational techniques will ensure a secure traceability enabling producers to characterize dairy products.     

Investigations on the compressive strength of silica fume concrete using statistical methods

The present paper deals with a mathematical model developed using statistical methods to predict the 28-day compressive strength of silica fume concrete with water-to-cementitious material (w/cm) ratios ranging from 0.3 to 0.42 and silica fume replacement percentages from 5 to 30. Strength results of 26 concrete mixes, on more than 300 test specimens, have been analyzed for statistical modeling. The ratios of compressive strengths between silica fume and control concrete have been related to silica fume replacement percentage. The expression, being derived with strength ratios and not with absolute values of strength, is independent of the specimen parameters and is applicable to all types of specimens. On examining the validity of the model with the results of previous researchers, it was observed that for results on both cubes and cylinders, predictions were obtained within 7.5% of the experimentally obtained values.     

Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

Applications of artificial neural networks in chemical engineering

A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection, signal processing, process modeling, and control. Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes certain types of neural networks that have proved to be effective in practical applications, mentions the advantages and disadvantages of using them, and presents four detailed chemical engineering applications. In the competitive field of modeling, ANN have secured a niche that now, after one decade, seems secure.     

Prediction of thermodynamic properties of polyvinylpyrrolidone solutions by artificial neural networks coupled with genetic algorithm

In the present study, the artificial neural networks coupled with the genetic algorithm (ANN–GA) models were used to predict the thermodynamic properties of polyvinylpyrrolidone (PVP) solutions in water and ethanol at various temperatures, mass fractions, and molecular weights of polymer. The genetic algorithm (GA) was used to find the best weights and biases of the network and improve the performance of ANNs. The proposed model was composed of three input variables including the temperature of the solution, the mass fraction, and molecular weight of the polymer. Density, viscosity, and surface tension of PVP solutions with various molecular weights (10,000, 25,000, and 40,000) in water and ethanol have been measured in the temperature range 20–55°C and various mass fractions of polymer. The ANN–GA models were trained by the experimental datasets and the prediction of density, surface tension, and viscosity of PVP solutions was performed using these models. The predicted values were compared with the experimental ones and the mean absolute relative error was less than 0.5% for the density and surface tension and about 3% for the viscosity of solutions.     

Experimental relationship between splitting tensile strength and compressive strength of GFRC and PFRC

This paper describes an experimental investigation into the relationship between the splitting tensile strength and compressive strength of glass fiber reinforced concrete (GFRC) and polypropylene fiber reinforced concrete (PFRC). The splitting tensile strength and compressive strength of GFRC and PFRC at 7, 28 and 90 days are used. Test results indicate that the addition of glass and polypropylene fibers to concrete increased the splitting tensile strength of concrete by approximately 20-50%, and the splitting tensile strength of GFRC and PFRC ranged from 9% to 13% of its compressive strength. Based on this investigation, a simple 0.5 power relationship between the splitting tensile strength and the compressive strength was derived for estimating the tensile strength of GFRC and PFRC.     

Generalization of strength versus water-cementitious ratio relationship to age

In this paper, an attempt is made to generalize Abrams' law to any given age. It is intended to enhance the applicability of this law for practical applications by covering 3 to 365 days range. The result makes the prediction of concrete strength more convenient. Two novel methodologies, parameter-trend-regression and four-parameter-optimization methodology, have been proposed to extend the Abrams' formula and a power formula to any given age without collecting data at that age. Experimental data from several different sources are used to validate the reliability of these methodologies. As a result of the analysis presented in this study, a set of generalized water-cementitious ratio formulas is proposed for concrete with limited replacement percentage of fly ash. It is shown that the generalized formulas agree with the experimental data better than the original formulas do.     

人工神经网络及其在耐火材料研究中的应用

介绍了人工神经网络的发展、特点、模型及其在材料研究中的应用现状;并以反应烧结原位ZrO2-SiC（p）材料中SiC生成量的拟合预报为例,讨论了其在耐火材料研究中的应用。     

人工神经网络在化学工程中的应用

介绍了人工神经网络在化学工程中的应用，讨论了应用中的优点和局限性，预示了其应用的发展前景。     

ZrC/C aerogel with high compressive strength by a carbothermic process

The ZrC/C aerogel is successfully prepared through copolymerization sols combined with carbothermal reduction method, using the ZrOC and phenol formaldehyde (PF) as the sols, and hexamethylenetetramine (HMTA) as cross-linker, respectively. The effects of heating treatment temperatures on physical and thermal properties of the aerogels are also investigated. XRD, SEM, and XPS measurements were adopted to characterize the morphology and microstructure of aerogels. Aerogels displayed low bulk density (0.262?0.379 g/cm³), relatively low thermal conductivity (0.0896?0.1064 W/m?K), and high compressive strength (0.87–4.42 MPa). XRD results show that the aerogel is composed of ZrC and ZrO₂ phases at 1400 °C and ZrC at 1500 °C or even high temperature, respectively, indicating that phase transformation of ZrO₂ into ZrC. XPS results demonstrated that the element Zr, C, and O are based on ZrOC, ZrC, and ZrO valence band binding, respectively. ZrC/C aerogels with excellent physical and thermal properties may be used in high-temperature field soon.