Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.     

Nonmonotonic Coupled Dissolution-Precipitation Reactions at the Mineral–Water Interface

Dissolution is inherent to fluid-mineral systems. Yet its impact on minerals reacting with electrolytes is overlooked. Here, a novel nonmonotonic behavior for the surface interactions of carbonates (calcite and Mg-calcite) with organic acids is reported. Applying a bioinspired approach, Mg-calcite sensors via amorphous precursors, avoiding any preconditioning with functional groups are synthesized. A quartz crystal microbalance is used to study the mass changes of the mineral on contact with organic acids under varying ionic conditions, temperatures, and flow velocities. Supported by confocal Raman microscopy and potentiometric titrations, nonmonotonous mass developments are found as a function of Ca²⁺ concentration and flowrate, and attributed to three coupled chemical reactions: i) carbonate dissolution via Ca²⁺ ion complexation with organic molecules, and the formation of organo-calcium compounds as ii) a surface phase at the mineral–water interface, and iii) particles in the bulk fluid. These processes depend on local ion contents and the precipitation onset (i.e., saturation index) of organo-calcium salts, both of which substantially differ in the bulk fluid and in the fluid boundary layer at mineral interfaces. This continuum between dissolution and precipitation provides a conceptual framework to address reactions at mineral interfacial across disciplines including biomineralization, ocean acidification and reservoir geochemistry.     

Synthesis,characterization, rheological and thermal behavior of metallocene ethylene − norbornene copolymers with low norbornene content using pentafluorophenol modified methylaluminoxane

Ethylene ? norbornene copolymers were synthesized using rac‐ethylene bis(indenyl) zirconium dichloride/pentafluorophenol modified methylaluminoxane. First, the effect of using a modifier in combination with a low ratio of Al/Zr on the catalyst activity and co‐monomer incorporation was studied. The results of copolymerization reveal a 20% co‐monomer incorporation improvement and a rise of activity by 2‐fold in the presence of the modifier. Rheological measurements show a higher molecular weight in copolymers synthesized using modified methylaluminoxane. The alternative and dyad block microstructures of copolymers become possible in the case of a norbornene content of more than 14 mol%. Second, the effect of co‐monomer content on the rheological and thermal behavior of the synthesized copolymers was investigated. The results of the rheological study indicate a lower molecular weight in samples containing a higher norbornene content. Dynamic mechanical thermal analysis confirms the influence of different microstructures on the glass transition temperature. The crystal structure of copolymers having a higher molecular weight is emphasized using wide angle X‐ray scattering and DSC even with a greater incorporation of norbornene. © 2015 Society of Chemical Industry     

Neural network modeling and analysis of the material removal process during laser machining

To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.     

Experimental Investigation and Computational Fluid Dynamics Simulation of the Magnetite Concentrate Reduction Using Methane-Oxygen Flame in a Laboratory Flash Reactor

Metallurgical and Materials Transactions B - An experimental investigation of the reduction of magnetite concentrate particles was conducted in a laboratory-scale flash reactor representing a novel...     

Modeling of thermodynamic properties of carrot product using ALO,GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer

Engineering with Computers - In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s)...     

Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.