Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

YesPolypropylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75 °C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR)

Steady State and Dynamic Modeling of Spiral Wound Wastewater Reverse Osmosis Process

yesReverse osmosis (RO)is one of the most important technologies used in wastewater treatment plants due to high contaminant rejection and low utilization of energy in comparison to other treatment procedures. For single-component spiral-wound reverse osmosis membrane process, one dimensional steady state and dynamic mathematical models have been developed based on the solution-diffusion model coupled with the concentration polarization mechanism. The model has been validated against reported data for wastewater treatment from literature at steady state conditions. Detailed simulation using the dynamic model has been carried out in order to gain deeper insight of the process. The effect of feed flow rate, pressure, temperature and concentration of pollutants on the performance of the process measured in terms of salt rejection, recovery ratio and permeate flux has been investigated.The full text will be available at the end of the publisher's embarg

Modeling of a spiral-wound reverse osmosis process and parameter estimation

YesReverse osmosis system has been widely used for the separation of organic and non-organic pollutants present in wastewater. The main aim of this study is to develop a one dimensional steady state model based on the three-parameter Spiegler-Kedem methodology using the gPROMS software and validate it by assessing the performance of membrane rejection for the separation data of aqueous solutions of phenol under different concentrations and pressures. Considerations of the variance of pressure, flow rate, solute concentration, solvent and solute fluxes and mass transfer coefficient along the feed channel were included in the model. Furthermore, an optimization methodology for the gEST parameter estimation tool has been developed in the gPROMS and used with experimental data in order to estimate the best values of the separation membrane parameters and the friction parameter. The simulation results of this model have been corroborated by experimental data

Development and Validation of N-nitrosamine Rejection Mathematical Model Using a Spiral-wound Reverse Osmosis Process

yesIn this paper, a one-dimensional mathematical model based on coupled differential and algebraic equations has been developed for analysing the separation mechanism of a N-nitrosamine in a spiral-wound reverse osmosis process. The model is based on Spiegler and Kedem’s work on mass transport and Darcy’s law and concentration polarization to analyse the pressure drop and mass transfer coefficient in the module feed channel respectively. The model is built using the gPROMS software suite and validated using N-nitrosamine rejection experimental data from the literature, obtained by using a pilot-scale cross-flow reverse osmosis filtration system. Analysis results derived from the model corroborate experimental data

Wastewater Treatment by Spiral Wound Reverse Osmosis: Development and Validation of a Two Dimensional Process Model

yesReverse osmosis (RO) has become a significant method for removing salts and organic compounds from seawater and wastewater in recent decades. Spiral-wound module has been widely used due to a number of special features such as high packing density, premium separation and low operating cost. In this paper, a two-dimensional mathematical model is developed for the transport of dilute aqueous solutions through a spiral-wound RO module and the operational characteristics of the process under steady state conditions are analysed. The model is based on the solution-diffusion model coupled with the concentration polarization mechanism. This model yields a set of Differential and Algebraic Equations (DAEs), which are solved using the gPROMS software. The model is validated using experimental data from the literature for the rejection of dimethylphenol as solute in aqueous solutions. The model is then used to simulate the process under steady state conditions to gain deeper insight of the process

Optimal reverse osmosis network configuration for the rejection of dimethylphenol from wastewater

YesReverse osmosis (RO) has long been recognised as an efficient separation method for treating and removing harmful pollutants, such as dimethylphenol in wastewater treatment. This research aims to study the effects of RO network configuration of three modules of a wastewater treatment system using a spiral-wound RO membrane for the removal of dimethylphenol from its aqueous solution at different feed concentrations. The methodologies used for this research are based on simulation and optimisation studies carried out using a new simplified model. This takes into account the solution-diffusion model and film theory to express the transport phenomena of both solvent and solute through the membrane and estimate the concentration polarization impact respectively. This model is validated by direct comparison with experimental data derived from the literature and which includes dimethylphenol rejection method performed on a small-scale commercial single spiral-wound RO membrane system at different operating conditions. The new model is finally implemented to identify the optimal module configuration and operating conditions that achieve higher rejection after testing the impact of RO configuration. The optimisation model has been formulated to maximize the rejection parameters under optimal operating conditions of inlet feed flow rate, pressure and temperature for a given set of inlet feed concentration. Also, the optimisation model has been subjected to a number of upper and lower limits of decision variables, which include the inlet pressure, flow rate and temperature. In addition, the model takes into account the pressure loss constraint along the membrane length commensurate with the manufacturer’s specifications. The research clearly shows that the parallel configuration yields optimal dimethylphenol rejection with lower pressure loss

Cost evaluation and optimisation of hybrid multi effect distillation and reverse osmosis system for seawater desalination

YesIn this research, the effect of operating parameters on the fresh water production cost of hybrid Multi Effect Distillation (MED) and Reverse Osmosis (RO) system is investigated. To achieve this, an earlier comprehensive model developed by the authors for MED + RO system is combined with two full-scale cost models of MED and RO processes collected from the literature. Using the economic model, the variation of the overall fresh water cost with respect to some operating conditions, namely steam temperature and steam flow rate for the MED process and inlet pressure and flow rate for the RO process, is accurately investigated. Then, the hybrid process model is incorporated into a single-objective non-linear optimisation framework to minimise the fresh water cost by finding the optimal values of the above operating conditions. The optimisation results confirm the economic feasibility of the proposed hybrid seawater desalination plant

Design and economic evaluation of solar-powered hybrid multi effect and reverse osmosis system for seawater desalination

YesReducing the cost of fresh water has always been a major concern in the desalination industry. A solar powered hybrid multi-effect distillation and reverse osmosis desalination plant (MED+RO) has been designed and optimised from an economical point of view in a previous work by the same authors. In the present study, the possibility of coupling the desalination plant with a photovoltaic (PV) solar farm is investigated, with the aim of generating electricity at low cost and in a sustainable way. A detailed mathematical model for the PV system has been implemented from the literature. Interestingly, the model can predict the cost of the PV system in terms of capital cost and electricity cost per kWh considering the input data of solar irradiation, duration of daylight and technical specification of a real solar module. Consequently, the solar PV model has been combined with the desalination model, which enables to estimate the cost of fresh water per cubic meter. Data about four locations, namely Isola di Pantelleria (IT), Las Palmas (ES), Abu Dhabi (UAE), and Perth (AUS), have been used to economically test the feasibility of installing the proposed plant, and especially of the PV solar farm

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

Numerical modelling and sensitivity analysis of natural draft cooling towers

YesCooling towers are a relatively inexpensive and consistent method of ejecting heat from several industries such as thermal power plants, refineries, and food processing. In this research, an earlier model from literature was to be validated across three different case studies. Unlike previous models, this model considers the height of the fill as the discretised domain, which produces results that give it in a distribution form along the height of the tower. As there are limitations with the software used (gPROMS) where differential equations with respect to independent variables in the numerator and denominator cannot be solved, a derivative of the saturation vapour pressure with respect to the temperature of the air was presented. Results shown were in agreement with the literature and a parametric sensitivity analysis of the cooling tower design and operating parameters were undertaken. In this work the height of fill, mass flowrates of water and air were studied with respect to sensitivity analysis. Results had shown large variations in the outlet temperatures of the water and air if the mass flows of water and air were significantly reduced. However, upon high values of either variable had shown only small gains in the rejection of heat from the water stream. With respect to the height of the fill, at larger heights of the fill, the outlet water temperature had reduced significantly. From a cost perspective, it was found that a change in the water flowrate had incurred the largest cost penalty with a 1% increase in flowrate had increased the average operating cost by 1.2%. In comparison, a change in air flowrate where a 1% increase in flowrate had yielded an average of 0.4% increase in operating cost