State of the Art in the Development of Adaptive Soft Sensors based on Just-In-Time Models

Data-driven soft sensors have gained popularity due to availability of the recorded historical plant data. The success stories of the implementations of soft sensors, however, involved some practical difficulties. Even if a good soft sensor is successfully developed, its predictive performance will gradually deteriorate after a certain time due to changes in the state of plants and process characteristics, such as catalyst deactivation and sensor and process drifts due to equipment ageing, fouling, clogging and wear, changes of raw materials and so on. To get soft sensor automatically updated, different kinds of methods have been introduced, such as Kalman filter, moving window average, recursive and ensemble methods. However, these methods have some drawbacks which motivate the development and implementation of just-in-time (JIT) model based adaptive soft sensor. This paper aims to report the current status of adaptive soft sensors based on just-in-time modelling approach. Critical review and discussion on the original and modified algorithms of the JIT modelling approach are presented. Proposed topics for future research and development are also outlined to provide a road map on the developing improved and more practical adaptive soft sensors based on JIT models

An integrated approach to artificial neural network based process modelling

ANN technology exploded into the world of process modelling and control in the late 1980’s. The technology shows great promise and is seen as a technology that could provide models for most systems without the need to understand the fundamental behaviour or relationships among the process variables. Today, ANN applications have been applied successfully in a number of areas of process modelling and control, with the best-established applications being in the area of inferential measurements or soft sensors.Unfortunately, ‘the free lunch did not have much meat’. Overtime, people focused more on the true capabilities and power of ANN, the ability to model nonlinear relationships in data without having to define the form of the nonlinearity. However, there is often a tendency to merely plug in the data, turn the ANN training software on, and blindly accept the results. This is probably inevitable since, to date, there are no textbooks or scientific journal papers providing an integrated and systematic approach for ANN model development addressing pre-modelling, training and postmodelling stages. Therefore, addressing issues in those three phases of ANN model development is essential to support and to improve further applications of ANN technology in the area of process modelling and control.The model development issues in pre-modelling and training phases were addressed by reviewing current practice and existing techniques. For each issue, a novel method was proposed to improve the performance of ANN models. The new approaches were tested in a variety of benchmarking studies using artificial samples and coal property datasets from power station boilers.The research work in the post-modelling stage analysis which emphasises on taking the lid off black box model, proposes a novel technique to extract knowledge from the models and simultaneously obtain better understanding of the process. Postmodelling phase issues were addressed thoroughly including construction of prediction limit, sensitivity analysis and development of mathematical representation of the trained ANN model.Confidence intervals of the ANN models were analysed to construct the prediction boundary of the model. This analysis provides useful information related to interpolation and extrapolation of the model. It also highlighted how good the ANN models can be used for extrapolation purposes.An effort based on sensitivity analysis of hidden layers is also proposed to understand the behaviours of the ANN models. Using this technique, knowledge and information are retrieved from the developed models. A comparative study of the proposed techniques and the current practice was also presented.The last topic addressed in this thesis is knowledge extraction of ANN models using mathematical analysis of the hidden layers. The proposed analysis is applied in order to open the black box of the ANN models and is implemented to simulated and real historical plant data so that useful information from those data and better understanding of the process are obtained.All in all, efforts have been made in this thesis to minimise the use of abstract mathematical language and in some cases, simplify the language so that ANN modelling theory can be understood by a wider range of audience, especially the new practitioners in ANN based modelling and control. It is hoped that the insight provided in the dissertation will provide an integrated approach to pre-modelling, training and post-modelling stages of ANN models. This ‘new guideline’ of ANN model development is unique and beneficial, providing a systematic framework for the preparation, design, evaluation and implementation of ANN models in process modelling and control in particular and prediction / forecasting tool in general

A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models

This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations

Product Concentration, Yield and Productivity in Anaerobic Digestion to Produce Short Chain Organic Acids : A Critical Analysis of Literature Data

Funding: This study was funded by LEVERHULME TRUST. Serena Simonetti, a Leverhulme Trust Doctoral Scholar, is part of the 15 PhD scholarships of the “Leverhulme Centre for Doctoral Training in Sustainable Production of Chemicals and Materials” at University of Aberdeen (Scotland, UK).Peer reviewedPublisher PD

Extraction and solubility modelling of Sarawak Black Pepper Oil in Supercritical Carbon Dioxide

The solubility of Black Pepper Oil (BPO) was measured in Supercritical Carbon Dioxide (SC-CO2). The temperatures and pressures of the extraction were chosen in the range of 313 – 333K and 100 - 300 bars, respectively. The solubilities attained ranged between 0.27 x 10-5 to 2.88 x 10-5 g extract/g CO2. 5 different empirical models were selected to predict the solubility of BPO in SC-CO2. Among the 5 empirical models, Belghait model resulted in the lowest but best absolute average relative deviation (AARD) of 14.90%

Septage treatment using vertical-flow engineered wetland: A critical review

Septage, which is a mixture of sludge, scum and liquid, is a type of faecal sludge that is specifically removed from an individual septic tank. Their biochemical stability and high concentration of solids and nutrients are the major technical challenges towards effective treatments in the existing wastewater treatment systems. A subsurface vertical-flow engineered wetland (VFEW) is, therefore, introduced as a feasible decentralized septage treatment option for small or medium communities due to its abilities in achieving excellent treatment and energy efficiency and reasonable cost through a simple operation. In general, the VFEW removes suspended solids, organic matter and nitrogenous components constituted in raw septage efficiently and sustainably. This paper presents a critical review on the state-of-the art of septage treatment using vertical-flow engineered wetland with regards to their characteristics and operation. The system-factor such as substrate profile and operational factors such as solid loading rate (SLR) and frequency of loading have been generally agreed as major factors governing the effectiveness of VFEWs. The selection of substrates is crucial to ensure a long-term usability of the VFEW with regards to the clogging phenomenon. The SLR, which ranged from 30 to 250 kg TS m-2 y-1, is of great importance to the treatment capability. The frequency of loading determines the rate of oxygen renewal, microbial growth and mineralization of the accumulated sludge deposit within the VFEW system. Future research directions and recommendations are also outlined

Joint Projects and Assessments of Chemical Engineering Units: An Approach to Enhance Student Learning

The ability to apply knowledge of basic science and engineering fundamentals associated with each and every subject learnt in their undergraduate program is an essential attribute of the chemical engineering graduate. Even though principles of chemical engineering are distributed across the units from first to fourth year, a chemical engineer should be able to relate all these principles to solve chemical engineering problems. However, relating these principles and drawing parallels between these subjects is not an easy task unless during their undergraduate study, a chemical engineering student was given training in doing projects involving principles across a variety of units. In view of the above necessity, chemical engineering at Curtin University has implemented combined projects and joint assessments between two units which not only provides an avenue for students to experience relating concepts they learnt from different units, but also reduces the work load for both teaching staff and students. In this paper, two experiences of having combined projects and joint assessments between units in chemical engineering program are presented and discussed

Improved process modifications of aqueous ammonia-based CO2 capture system

Extensive research works on CO2 capture process using MEA have been carried out and showed promising results. Nevertheless, it has been acknowledged that the use of MEA is associated with high cost, solvent degradation issues and corrosion. The issues above have motivated researchers to explore and test other potential solvents such as aqueous ammonia (NH3). As result, NH3 based CO2 capture systems have recently attracted much attention as an alternative to MEA based counterparts. Despite their encouraging applications, high volatility of NH3 raise concerns on the energy requirement related to the solvent recovery. Consequently, energy efficient NH3 based CO2 capture systems by modifying the process is desirable. This study, therefore, aims to propose and evaluate three different stand-alone process configurations of absorption-desorption processes in a NH3-based system and compare them with the traditional absorption-desorption system in respect to total energy consumption. These modifications include Rich Solvent Split (RSS), Lean Vapor Compression (LVC), and Rich Vapor Compression (RVC). Results indicate that among these three proposed process modifications, LVC led to the highest reboiler energy savings of 38.3% and total energy savings of 34.5% compared to NH3 based conventional configuration. These findings can serve as essential recommendations for further studies on and large-scale implementations of aqueous NH3 as a better solvent

Parametric Study of Experimental and CFD Simulation Based Hydrodynamics and Mass Transfer of Rotating Packed Bed: A Review

The emission of CO2 into the atmosphere is one of the major causes of the greenhouse effect, which has a devastating effect on the environment and human health. Therefore, the reduction of CO2 emission in high concentration is essential. The Rotating Packed Bed (RPB) reactor has gained a lot of attention in post-combustion CO2 capture due to its excellent rate of mass transfer and capture efficiency. To better understand the mechanisms underlying the process and ensure optimal design of RPB for CO2 absorption, elucidating its hydrodynamics is of paramount importance. Experimental investigations have been made in the past to study the hydrodynamics of RPB using advanced imaging and instrumental setups such as sensors and actuators. The employments of such instruments are still challenging due to the difficulties in their installation and placement in the RPB owing to the complex engineering design of the RPB. The hydrodynamics of the RPB can be affected by various operational parameters. However, all of them cannot be evaluated using a single instrumental setup. Therefore, the experimental setups generally result in a partial understanding of the flow behavior in the RPB. The cons and pros of experimental methods are reported and critically discussed in this paper. Computational Fluid Dynamics (CFD), on the other hand, is a powerful tool to visually understand the insights of the flow behavior in the RPB with accurate prediction. Moreover, the different multiphase and turbulence models employed to study the hydrodynamics of RPB have also been reviewed in-depth along with the advantages and disadvantages of each model. The models such as Sliding Mesh Model (SMM) and rotating reference frame model have been adopted for investigating the hydrodynamics of the RPB. The current research gaps and future research recommendations are also presented in this paper which can contribute to fill the existing gap for the CFD analysis of Rotating Packed Bed (RPB) for CO2 absorption

Modelling of Hydraulic Dynamics in Sludge Treatment Reed Beds with Moving Boundary Condition

The conventional method of simulation using fixed mesh method (FMM) of discretization is a well-known and trusted procedure in modelling hydraulic dynamics. However, new ideas of innovation in modelling should be advanced. The moving mesh method (MMM) has been considered as a novel approach in modelling hydraulic dynamics after depending on the existing simulation model for decades. The MMM is capable of describing the moving boundary condition of an actual wetland system due to water ponding. An idealized model should be able to simulate the actual hydraulic flows through the system with the corresponding porosity. Hence, a combination of MMM and FMM (MM-FMM) of discretization for hydraulic dynamics is studied in this project to model the flux with respect to water ponding scenario in a sludge treatment reed bed and unsaturated transient flow within the bed. Such method has evidently proved to simulate the actual hydraulic flows in contrast to conventional method. The application of MMM limits the maximum flux to keep within its saturated conductivity, thus reduces the effect of flow overprediction. Subsequently, the simulated results for hydraulic head and moisture content can be predicted for actual condition of different cases according to their respective fluxes