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.     

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.     

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.     

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.     

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.     

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.     

Preparation and characterization of high compressive strength foams from sheet glass

High compressive strength glass foams were produced using sheet glass cullet with the aid of 1 wt.% SiC powder, as gassing agent, and the incorporation of small amounts of an alkali earth aluminosilicate glass powder (AD), which is intrinsically prone to be crystallised to anorthite and diopside. The amount of SiC used as well as the mean particle sizes of the powders of both glasses and SiC were lower than those used in earlier studies. The experimental results showed that homogenous microstructures of large pores could be obtained by adding 1 wt.% SiC. The compressive strength of the glass foams was considerably increased when the incorporated AD-glass was higher than 1 wt.%. It is concluded that the presence of the AD glass is beneficial for the produced glass foams because of the formation of a well packed honeycomb structure which features an optimal distribution of pentagonal- and hexagonal-like shaped pores surrounded by dense struts. The crystallization of wollastonite and diopside inside the struts should also have a positive impact on the mechanical behaviour of the produced porous glass foams.     

Prediction model of compressive strength development of fly-ash concrete

Based on experimental results concerning the compressive strength development of concrete containing fly ash, the authors derived an estimation equation for compressive strength development. The equation can express coefficient , which indicates the activity of fly ash as a binder, in the form of a function of age, fly-ash content, and Blaine specific surface area of fly ash.

This equation is capable of explaining the increases in the early strength due to fly ash in place of part of fine aggregate, the decreases in the early strength due to fly ash in place of part of cement, the increases in the long-term strength due to pozzolanic reaction, the relationship between the fly-ash replacement ratio and the ratio of strength increase/decrease, and the effect of fly ash's Blaine specific surface area on the strength.     

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames

The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.