1,721,190 research outputs found
The Application Of Polymer Particles In Industrial Processes
Get PDFThe research in this thesis considers novel innovative developments in established
industrial processes that involve the use of recyclable polymeric particles as a
partial replacement for aqueous media and chemicals. The application of the
technology typically leads to water savings of over 80% and chemical savings of
over 25%. These industrial processes may be characterised in that are considered
inefficient and wasteful but nevertheless are considered economically vital. These
diverse industries including laundry cleaning, leather manufacturing, textile
garment processing, effluent treatment and metal beverage can manufacture.
The outcomes of this research have made significant contributions to industrial
best practice in such industries. In terms of academic research, the knowledge
created in this thesis provides the basis for the application of CFD-DEM modelling
to understand complex multi-phase and multi-component systems. In particular,
the thesis advocates the application of the Free Surface Lattice Boltzmann Method
for creating highly accurate simulations of multi-phase flow. In addition, the thesis
offers opportunities for further research in novel plasma micro-reactors and their
applications in diverse fields such as chemical synthesis, chemical engineering and
biotechnology. The nature of the research is multi-disciplinary, and involved
investigations across several fields including applied mathematics, biochemistry,
chemistry, physics, and engineering. The projects also involved scale up from
laboratory, pilot plant and full commercial scale production trials. Primary
objectives were investigated through a series of six published patents. The three
patents relating to the development of novel leather and textile processes were
solely conceived and executed by the author. The patent related to the
development of the plasma micro-reactor for ozone synthesis was conceived and
executed jointly by the author and Professor Will Zimmerman (Sheffield University).
The two patents related to the development of a novel metal cleaning and
treatment process was conceived and executed jointly by the author and Dr.
Robert Bird (Xeros Technology Group Limited)
Simulation and optimization of a continuous Reverse Osmosis desalination process for making fresh water
No full textDespite being a mature process, production of fresh water using desalination is still a challenge. Desalination is broadly divided into two categories; thermal desalination processes, such as multi-stage flash, and semi-permeable membrane process, such as Reverse Osmosis (RO). This work is aimed at developing correlations for water permeability coefficient (Kw) and salt permeability coefficient (Ks) as a function of feed salinity and pressure using experimental data for a continuous RO process. For three different feed salinities of 15, 25, and 35 g/L at two different pressures of 40 and 45 bara experimental values of Ksand kwvalues are taken from the literature. Planar and ellipsoidal least square methods are used to correlate kwand Ksas a function of feed salinity and pressure, which are then embedded within the continuous RO process model to evaluate the process performance in terms of maximising the recovery ratio while optimizing the area and pressure to get the desired freshwater salinity. gPROMS model builder is used to simulate and optimise the process
Design and Operation of Multistage Flash (MSF) Desalination: Advanced Control Strategies and Impact of Fouling. Design operation and control of multistage flash desalination processes: dynamic modelling of fouling, effect of non-condensable gases on venting system design and implementation of GMC and fuzzy control
Get PDFThe rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant
Current based condition monitoring of electromechanical systems. Model-free drive system current monitoring: faults detection and diagnosis through statistical features extraction and support vector machines classification.
Get PDFA non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems.
This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severityMinistry of Higher Education, Libya; Switchgear & Instruments Ltd
Dynamic optimisation and control of batch reactors. Development of a general model for batch reactors, dynamic optimisation of batch reactors under a variety of objectives and constraints and on-line tracking of optimal policies using different types of advanced control strategies.
No full textBatch reactor is an essential unit operation in almost all batch-processing
industries. Different types of reaction schemes (such as series, parallel and complex)
and different order of model complexity (short-cut, detailed, etc. ) result in different sets
of model equations and computer coding of all possible sets of model equations is
cumbersome and time consuming. In this work, therefore, a general computer program
(GBRM - General Batch Reactor Model) is developed to generate all possible sets of
equations automatically and as required. GBRM is tested for different types of reaction
schemes and for different order of model complexity and its flexibility is demonstrated.
The above GBRM computer program is lodged with Dr. I. M. Mujtaba.
One of the challenges in batch reactors is to ensure desired performance of
individual batch reactor operations. Depending on the requirement and the objective of
the process, optimisation in batch reactors leads to different types of optimisation
problems such as maximum conversion, minimum time and maximum profit problem.
The reactor temperature, jacket temperature and jacket flow rate are the main control
variables governing the process and these are optimised to ensure maximum benefit. In
this work, an extensive study on mainly conventional batch reactor optimisation is
carried out using GBRM coupled with efficient DAEs (Differential and Algebraic
Equations) solver, CVP (Control Vector Parameterisation) technique and SQP
(Successive Quadratic Programming) based optimisation technique. The safety,
environment and product quality issues are embedded in the optimisation problem
formulations in terms of constraints. A new approach for solving optimisation problem
with safety constraint is introduced. All types of optimisation problems mentioned
above are solved off-line, which results to optimal operating policies.
The off-line optimal operating policies obtained above are then implemented as
set points to be tracked on-line and various types of advanced controllers are designed
for this purpose. Both constant and dynamic set points tracking are considered in
designing the controllers. Here, neural networks are used in designing Direct Inverse
and Inverse-Model-Based Control (IMBC) strategies. In addition, the Generic Model
Control (GMC) coupled with on-line neural network heat release estimator (GMC-NN)
is also designed to track the optimal set points. For comparison purpose, conventional
Dual Mode (DM) strategy with PI and PID controllers is also designed. Robustness tests
for all types of controllers are carried out to find the best controller. The results
demonstrate the robustness of GMC-NN controller and promise neural controllers as
potential robust controllers for future. Finally, an integrated framework
(BATCH REACT) for modelling, simulation, optimisation and control of batch
reactors is proposed.University Sains Malaysi
Modelling and optimisation of oxidative desulphurization process for model sulphur compounds and heavy gas oil. Determination of Rate of Reaction and Partition Coefficient via Pilot Plant Experiment; Modelling of Oxidation and Solvent Extraction Processes; Heat Integration of Oxidation Process; Economic Evaluation of the Total Process.
No full textHeightened concerns for cleaner air and increasingly more stringent regulations on
sulphur content in transportation fuels will make desulphurization more and more
important. The sulphur problem is becoming more serious in general, particularly for
diesel fuels as the regulated sulphur content is getting an order of magnitude lower,
while the sulphur contents of crude oils are becoming higher. This thesis aimed to
develop a desulphurisation process (based on oxidation followed by extraction) with
high efficiency, selectivity and minimum energy consumption leading to minimum
environmental impact via laboratory batch experiments, mathematical modelling and
optimisation.
Deep desulphurization of model sulphur compounds (di-n-butyl sulphide, dimethyl
sulfoxide and dibenzothiophene) and heavy gas oils (HGO) derived from Libyan crude
oil were conducted. A series of batch experiments were carried out using a small reactor
operating at various temperatures (40 ¿ 100 0C) with hydrogen peroxide (H2O2) as
oxidant and formic acid (HCOOH) as catalyst. Kinetic models for the oxidation process
are then developed based on `total sulphur approach¿. Extraction of unoxidised and
oxidised gas oils was also investigated using methanol, dimethylformamide (DMF) and
N-methyl pyrolidone (NMP) as solvents. For each solvent, the `measures¿ such as: the
partition coefficient (KP), effectiveness factor (Kf) and extractor factor (Ef) are used to
select the best/effective solvent and to find the effective heavy gas oil/solvent ratios.
A CSTR model is then developed for the process for evaluating viability of the large
scale operation. It is noted that while the energy consumption and recovery issues could
be ignored for batch experiments these could not be ignored for large scale operation.
Large amount of heating is necessary even to carry out the reaction at 30-40 0C, the
recovery of which is very important for maximising the profitability of operation and
also to minimise environmental impact by reducing net CO2 release. Here the heat
integration of the oxidation process is considered to recover most of the external energy
input. However, this leads to putting a number of heat exchangers in the oxidation
process requiring capital investment. Optimisation problem is formulated using
gPROMS modelling tool to optimise some of the design and operating parameters (such
as reaction temperature, residence time and splitter ratio) of integrated process while
minimising an objective function which is a coupled function of capital and operating
costs involving design and operating parameters. Two cases are studied: where (i) HGO
and catalyst are fed as one feed stream and (ii) HGO and catalyst are treated as two feed
streams.
A liquid-liquid extraction model is then developed for the extraction of sulphur
compounds from the oxidised heavy gas oil. With the experimentally determined KP
multi stage liquid-liquid extraction process is modelled using gPROMS software and the
process is simulated for three different solvents at different oil/solvent ratios to select the best solvent, and to obtain the best heavy gas oil to solvent ratio and number of
extraction stages to reduce the sulphur content to less than 10 ppm.
Finally, an integrated oxidation and extraction steps of ODS process is developed based
on the batch experiments and modelling. The recovery of oxidant, catalyst and solvent
are considered and preliminary economic analysis for the integrated ODS process is
presented
Dynamic Modelling and Optimization of Polymerization Processes in Batch and Semi-batch Reactors. Dynamic Modelling and Optimization of Bulk Polymerization of Styrene, Solution Polymerization of MMA and Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors using Control Vector Parameterization Techniques.
No full textDynamic modelling and optimization of three different processes namely (a) bulk polymerization of styrene, (b) solution polymerization of methyl methacrylate (MMA) and (c) emulsion copolymerization of Styrene and MMA in batch and semi-batch reactors are the focus of this work. In this work, models are presented as sets of differential-algebraic equations describing the process. Different optimization problems such as (a) maximum conversion (Xn), (b) maximum number average molecular weight (Mn) and (c) minimum time to achieve the desired polymer molecular properties (defined as pre-specified values of monomer conversion and number average molecular weight) are formulated. Reactor temperature, jacket temperature, initial initiator concentration, monomer feed rate, initiator feed rate and surfactant feed rate are used as optimization variables in the optimization formulations. The dynamic optimization problems were converted into nonlinear programming problem using the CVP techniques which were solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software.
The process model used for bulk polystyrene polymerization in batch reactors, using 2, 2 azobisisobutyronitrile catalyst (AIBN) as initiator was improved by including the gel and glass effects. The results obtained from this work when compared with the previous study by other researcher which disregarded the gel and glass effect in their study which show that the batch time operation are significantly reduced while the amount of the initial initiator concentration required increases. Also, the termination rate constant decreases as the concentration of the mixture increases, resulting rapid monomer conversion.
The process model used for solution polymerization of methyl methacrylate (MMA) in batch reactors, using AIBN as the initiator and Toluene as the solvent was improved by including the free volume theory to calculate the initiator efficiency, f. The effects of different f was examined and compared with previous work which used a constant value of f 0.53. The results of these studies show that initiator efficiency, f is not constant but decreases with the increase of monomer conversion along the process.
The determination of optimal control trajectories for emulsion copolymerization of Styrene and MMA with the objective of maximizing the number average molecular weight (Mn) and overall conversion (Xn) were carried out in batch and semi-batch reactors. The initiator used in this work is Persulfate K2S2O8 and the surfactant is Sodium Dodecyl Sulfate (SDS). Reduction of the pre-batch time increases the Mn but decreases the conversion (Xn). The sooner the addition of monomer into the reactor, the earlier the growth of the polymer chain leading to higher Mn. Besides that, Mn also can be increased by decreasing the initial initiator concentration (Ci0). Less oligomeric radicals will be produced with low Ci0, leading to reduced polymerization loci thus lowering the overall conversion. On the other hand, increases of reaction temperature (Tr) will decrease the Mn since transfer coefficient is increased at higher Tr leading to increase of the monomeric radicals resulting in an increase in termination reaction
Neural network based hybrid modelling and MINLP based optimisation of MSF desalination process within gPROMS: Development of neural network based correlations for estimating temperature elevation due to salinity, hybrid modelling and MINLP based optimisation of design and operation parameters of MSF desalination process within gPROMS
Get PDFDesalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT).
Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature.
Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation.
Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand
Optimal design and operation of multivessel batch distillation with fixed product demand. Modelling, simulation and optimisation of design and operation parameters in multivessel batch distillation under fixed product demand scenario and strict product specifications using simple dynamic model in gPROMS.
No full textIncreased interest in unconventional batch distillation column configurations offers new opportunities for increasing the flexibility and energy efficiency of batch distillation. One configuration of particular interest is multivessel batch distillation column, which can be viewed as a generalization of all previously studied batch column configuration.
In this work, for the first time the optimal design and operation tasks are developed for multivessel batch distillation with strict product specifications under fixed product demand.
Also, in this work, two different operation schemes defined as STN (State Task Network) in terms of the option and numbers of off-cuts were considered for binary and ternary separation. Both the vapour load and number of stages in each column section together with the production sequence are optimised to achieve maximum profit function.
The performance of the multivessel batch distillation column is evaluated against the performance of conventional batch column with a simple dynamic model using binary and ternary mixtures. It has been found that profitability improves with the multivessel system in both separations.
gPROMS, a user-friendly, software is used for the modeling, simulation, and optimisation
Simulation and optimisation of industrial steam reformers. Development of models for both primary and secondary steam reformers and implementation of optimisation to improve both the performance of existing equipment and the design of future equipment.
No full textTraditionally the reactor is recognised as the `heart' of a chemical process system and
hence the focus on this part of the system is usually quite detailed. Steam reforming,
however, due to the `building block' nature of its reaction products is unusual and
generally is perceived as a `utility' to other reaction processes and hence the focus is drawn " towards the 'main' reaction processes of the system. Additionally as a `mature' process,
steam reforming is often treated as sufficiently defined for the requirements within the
overall chemical process.
For both primary and secondary steam reformers several models of varying complexity
were developed which allowed assessment of issues raised about previous models and
model improvements; drawing on the advancements in modelling that have not only
allowed the possibility of increasing the scope of simulations but also increased confidence
in the simulation results. Despite the complex nature of the steam reforming systems, a
surprisingly simplistic model is demonstrated to perform well, however, to improve on
existing designs and maximise the capability of current designs it is shown that more
complex models are required.
After model development the natural course is to optimisation. This is a powerful tool
which must be used carefully as significant issues remain around its employment. Despite
the remaining concerns, some simple optimisation cases showed the potential of the models
developed in this work and although not exhaustive demonstrated the benefits of
optimisation
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