A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers

Review on Advanced Control Technique in Batch Polymerization Reactor of Styrene

Polymerization process have a nonlinear nature since it exhibits a dynamic behavior throughout the process. Therefore, accurate modeling and control technique for the nonlinear process needs to be obtained

DYNAMICS AND CONTROL OF FORCED UNSTEADY-STATE CATALYTIC REACTORS

This research deals with the dynamics and control of forced unsteady-state catalytic reactors and it is focused on two topics: 1. auto-thermal after-treatment of lean VOC mixtures. Two reactor configurations have been taken into consideration: the reverse-flow reactor (RFR), where the flow direction is periodically changed, and the network of two or three reactors (RN), where the flow direction remains the same, but the feeding position is periodically changed, thus simulating a moving bed. This study (§3) has been organised as follows: - modelling of the two reactor configurations and study of the influence of the main operating parameters (§3.1 and §3.2). As the RFR shows higher stability with respect to disturbances in the feed a deeper investigation has been carried out on this device; - optimisation of the RFR. A simplified model has been used for this analysis in order to strongly reduce the computational effort which is required by detailed models. It has been pointed out that both heat capacity and thermal conductivity of the catalyst play a role, not less important than kinetic activity, strongly influencing the minimum inlet VOC concentration required for autothermal operation (§3.3); - experimental validation of the modelling results in a bench-scale RFR with reduced influence of the wall effects. This activity has been carried at the Departamento de Ingeniería Química y Tecnología del Medio Ambiente-Universidad de Oviedo (Spain) in the framework of the Research Project "Azioni Integrate Italia-Spagna", granted by the Italian Ministry of Research (MIUR). In addition to the intrinsecally dynamic behaviour of the RFR, one must deal with unexpected external perturbations (feed concentration, composition and temperature) which may lead to reactor extinction or catalyst overheating. In order to avoid these problems it is necessary to implement some closed-loop control strategy based on the measurement of the inlet concentration (and composition) and the outlet conversion. This study has been organised as follows: - a model-based soft-sensor (observer) has been developed, in order to quickly and reliably estimate the feed composition from some temperature measurements in the reactor, thus avoiding expensive hardware sensors and time consuming on-line measurements. As deriving an observer from a detailed model is an overwhelming task, a simplified model has been developed and validated in a medium size RFR. This research has been carried out in cooperation with prof. H. Hammoury and D. Schweich of the CPE-Lyon, France (§4.1); - a Model Based control strategy has been proposed and tested to prevent reaction extinction and catalyst overheating (§4.2); 2. enhancement of conversion and selectivity in exothermic, equilibriumlimited reactions. Methanol synthesis and syngas prouction by partial oxidation of methane have been considered as test reactions. This section has been organised as follows: - modelling of the two processes in the two reactor configurations previously described. The influence of the main operating conditions has been addressed with the aim to optimise the two processes. As the RN has shown higher conversion and selectivity with respect to the RFR, in the following the research will be focused on this device (§5); - a simple open loop control policy, which can be useful for a safe startup, has been also tested to study the response of the RN to disturbances on the input parameters, showing that a more robust control strategy is needed for this application; - if a tight control on the outlet product conversion is needed, a Model Predictive Control scheme (MPC) should be used, varying the switching time to maximise the conversion and the selectivity of the reactor. The on-line optimisation requires a simplified model and a Neural Network based model has been developed (§6

Neural Network-based Hybrid Estimator for Estimating Concentration in Ethylene Polymerization Process: An Applicable Approach

Estimation of a monomer concentration of an ethylene polymerization process has been a challenging problem due to its highly nonlinear behavior and interaction among state variables.  Applying of an extended Kalman filter (EKF) to provide the estimates of the concentration based on measured bed temperatures has usually been prone to errors. Here, alternatively, neural network-based hybrid estimators have been developed and classified into three structures which integrating of either EKF or Kalman filter (KF) to neural network (NN) to provide the estimates. The NNs are integrated to provide the estimates’ error or concentration’s estimates corresponding to individual structure for reducing the estimation error. Simulation results have shown that the hybrid estimators can provide good estimates under nominal condition and disturbance cases. However, in dealing with noises, the NN-KF hybrid estimator gives superior robustness with smooth and accurate estimated values

Editorial: Special Issue contributed by the 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008

The 10th International Chemical and Biological Engineering Conference - CHEMPOR 2008, was held in Braga, Portugal, from the 4th to the 6th of September, 2008. The conference was jointly organized by the University of Minho, the “Ordem dos Engenheiros,” and the Institute for Biotechnology and Bioengineeing, with the support of “Sociedade Portuguesa de Qu´ımica” and “Sociedade Portuguesa de Biotecnologia”. The CHEMPOR series traditionally brings together both young and established researchers and end users to discuss recent developments in different areas of Chemical Engineering. The scope of this edition was extended to Biological Engineering research. One of the major core areas of the conference program was life quality, due to the importance that Chemical and Biological Engineering plays in this area. “Integration of Life Sciences & Engineering” and “Sustainable Process-Product Development through Green Chemistry” were two of the leading themes with papers addressing such important issues. This was complemented with additional leading themes including “Advancing the Chemical and Biological Engineering Fundamentals,” “Multi-Scale and/or Multi-Disciplinary Approach to Process-Product Innovation”, “Systematic Methods and Tools for Managing the Complexity”, and “Educating Chemical and Biological Engineers for Coming Challenges.” Papers contributed for this special issue represent a good sample of the important themes that were addressed. This special issue presents a set of fifteen selected research papers, which have undergone the peer-review process of Chemical Product and Process Modeling journal. We wish to thank the authors who have contributed to yield a high scientific standard to this special issue. We also extend our gratefulness to all reviewers, through their dedicated efforts, having assisted us in this task.Uminho -Universidade do Minh

Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models

One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C)

Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

Heterogeneous, multi-component materials such as industrial tailings or by-products, along with naturally occurring materials, such as ores, have been intensively investigated as candidate oxygen carriers for chemical-looping processes. However, these materials have highly variable compositions, and this strongly influences their chemical-looping performance. Here, using machine learning techniques, we estimate the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping. Experimental data for 19 manganese ores chosen as potential chemical-looping oxygen carriers were used to create a so-called training database. This database has been used to train several supervised artificial neural network models (ANN), which were used to predict the reactivity of the oxygen carriers with different fuels and the oxygen transfer capacity with only the knowledge of reactor bed temperature, elemental composition, and mechanical properties of the manganese ores. This novel approach explores ways of dealing with the training dataset, learning algorithms and topology of ANN models to achieve enhanced prediction precision. Stacked neural networks with a bootstrap resampling technique have been applied to achieve high precision and robustness on new input data, and the confidence intervals were used to assess the precision of these predictions. The current results indicate that the best trained ANNs can produce highly accurate predictions for both the training database and the unseen data with the high coefficient of determination (R2 = 0.94) and low mean absolute error (MAE = 0.057). We envision that the application of these ANNs and other machine learning algorithms will accelerate the development of oxygen carrying materials for a range of chemical-looping applications and offer a rapid screening tool for new potential oxygen carriers

Input/Output Linearization for a Real-Time pH Control: Application on Basic Wastewater Neutralization by Carbon Dioxide in a Fed-Batch Bubble Column Reactor

A model-based application for pH regulation in a pilot unit of wastewater treatment by carbon dioxide gas is presented.  A reactor pH is an important factor to enhance the gas absorption of carbon dioxide bubbles in an alkaline wastewater, and it needs to operate within a tight pH range.  Under a fed-batch operation mode, the reactor behavior has unstable dynamics resulting in a difficulty to achieve the pH target by manipulating the basic influent feed rate.  To solve the problem, an input/output (I/O) linearization is applied because it provides excellent the setpoint trackability with a few numbers of tuning parameters required. The first principles approach is employed for reactor modeling.  The model is then used in the I/O feedback formulation.  Control performance is evaluated through a real-time implementation to track the desired pH target in comparison with a two-degree-of-freedom control scheme used as a compared case.  The developed control system proficiently forces the output to the pH target and also improves the control performances

Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production

A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability

Online Implementation Of Imc Based Pid Controller In Batch Esterification Reactor.

Esterification is one of important process that operates in a batch reactor. Since the inherent nonlinear properties of this batch reactor, the control of such reactor to ensure an efficient operation remains a great challenge