The Optimal Implementation of On-Line Optimization for Chemical and Refinery Processes.

On-line optimization is an effective approach for process operation and economic improvement and source reduction in chemical and refinery processes. On-line optimization involves three steps of work as: data validation, parameter estimation, and economic optimization. This research evaluated statistical algorithms for gross error detection, data reconciliation, and parameter estimation, and developed an open-form steady state process model for the Monsanto designed sulfuric acid process of IMC Agrico Company. The plant model was used to demonstrate improved economics and reduced emissions from on-line optimization and to test the methodology of on-line optimization. Also, a modified compensation strategy was proposed to improve the misrectification of data reconciliation algorithms and it was compared with measurement test method. In addition, two ways to conduct on-line optimization were studied. One required two separated optimization problems to update parameters, and the other combined data validation and parameter estimation into one optimization problem. Two-step estimation demonstrated a better performance in estimation accuracy than one-step estimation for sulfuric acid process, while one-step estimation required less computation time. The measurement test method, Tjoa-Biegler\u27 contaminated Gaussian distribution method, and robust method were evaluated theoretically and numerically to compare the performance of these methods. Results from these evaluation were used to recommend the best way to conduct on-line optimization. The optimal procedure is to conduct combined gross error detection and data reconciliation to detect and rectify gross errors in plant data from DCS using Tjoa-Biegler\u27s method or robust method. This step generates a set of measurements containing only random errors which is used for simultaneous data reconciliation and parameter estimation using the least squares method (the normal distribution). Updated parameters are used in the plant model for economic optimization that generates optimal set points for DCS. Applying this procedure to the Monsanto sulfuric acid plant had an increased profit of 3% over current operating condition and an emission reduction of 10% which is consistent with other reported applications. Also, this optimal procedure to conduct on-line optimization has been incorporated into an interactive on-line optimization program which used a window interface developed with Visual Basic and GAMS to solve the nonlinear optimization problems. This program is to be available through the EPA Technology Tool Program

Modelling and data validation for the energy analysis of absorption refrigeration systems

Data validation and reconciliation techniques have been extensively used in the process industry to improve the data accuracy. These techniques exploit the redundancy in the measurements in order to obtain a set of adjusted measurements that satisfy the plant model. Nevertheless, not many applications deal with closed cycles with complex connectivity and recycle loops, as in absorption refrigeration cycles. This thesis proposes a methodology for the steady-state data validation of absorption refrigeration systems. This methodology includes the identification of steady-state, resolution of the data reconciliation and parameter estimation problems and the detection and elimination of gross errors. The methodology developed through this thesis will be useful for generating a set of coherent measurements and operation parameters of an absorption chiller for downstream applications: performance calculation, development of empirical models, optimisation, etc. The methodology is demonstrated using experimental data of different types of absorption refrigeration systems with different levels of redundancy.Los procedimientos de validación y reconciliación de datos se han utilizado en la industria de procesos para mejorar la precisión de los datos. Estos procedimientos aprovechan la redundancia enlas mediciones para obtener un conjunto de datos ajustados que satisfacen el modelo de la planta. Sin embargo, no hay muchas aplicaciones que traten con ciclos cerrados, y configuraciones complejas, como los ciclos de refrigeración por absorción. Esta tesis propone una metodología para la validación de datos en estado estacionario de enfriadoras de absorción. Estametodología incluye la identificación del estado estacionario, la resolución de los problemas de reconciliación de datos y estimación de parámetrosy la detección de errores sistemáticos. Esta metodología será útil para generar un conjunto de medidas coherentes para aplicaciones como: cálculo de prestaciones, desarrollo de modelos empíricos, optimización, etc. La metodología es demostrada utilizando datos experimentales de diferentes enfriadoras de absorción, con diferentes niveles de redundancia

Issues in on-line optimisation

In general, on-line optimisation can be defined as the on-line process of finding the optimum set-points of the system. Several areas might be concerned in this procedure. This thesis evaluates algorithms for on-line Optimisation. Techniques for steady-state detection, static data reconciliation, gross error detection and steady-state optimisation are presented and implemented separately and within an on-line optimisation methodology. It has been acknowledged for some time now that the estimation of derivative information is probably the major drawback of the steady-state optimisation technique considered here: the ISOPE algorithm. This thesis investigates the requirements of these derivatives, methods proposed to estimate them, and presents some attempts to overcome some related problems. Also a modified version of the dynamic model identification method that uses a nonlinear model representation is proposed, and compared under simulation with other available techniques. In the same context, an alternative method based on Artificial Neural Networks to estimate the derivatives is also implemented and tested. Often, rigorous steady-state detection is crucial for process performance assessment, simulation, optimisation and control. In general, at steady-state data is collected for safe, beneficial and rational management of processes. A method for automatic detection of steady-state in multivariable processes is implemented and tested. The technique is applied on a dynamic model of a chemical reactor. The presence of errors in process measurements can invalidate the potential gains obtained from advanced optimisation and control techniques. Data reconciliation and gross error detection methods are used to reduce the inaccuracies of these measurements. The implementation and application of static data reconciliation and gross error detection techniques in this thesis show a noticeable improvement in the operation of the system, and general control system performance. The various algorithms mentioned above are successfully implemented and tested under simulation. It is illustrated that in some cases, it is possible to use steady state detection in conjunction with data reconciliation, gross error detection, parameter estimation and optimisation, to form an on-line optimisation methodology. The methodology was tested on a dynamic model of a chemical reactor

Data cleaning and knowledge discovery in process data

This dissertation presents several methods for overcoming the Big Data challenges, with an emphasis on data cleaning and knowledge discovery in process data. Data cleaning and knowledge discovery is chosen as a main research area here due to its importance from both theoretical and practical points of view. Theoretical background and recent developments of data cleaning methods are reviewed from four aspects: missing data imputation, outlier detection, noise removal and time delay estimation. Moreover, the impact of contaminated data on model performance and corresponding improvement obtained by data cleaning methods are analyzed through both simulated and industrial case studies. The results provide a starting point for further advanced methodology development. It is hard to find a universally applicable method for data cleaning since every data set may have its own distinctive features. Thus, we have to customize available methods so that the quality of the data set is guaranteed. An integrated data cleaning scheme is proposed, which incorporates model building and performance evaluation, to provide guidance in tuning the parameters of data cleaning methods and prevent over-cleaning. A case study based on industrial data has been used to verify the feasibility and effectiveness of the proposed new method, during which a partial least squares (PLS) model was built and three univariate data cleaning procedures is tested. A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier (AO) and innovational outlier (IO) detection problems in dynamic process data set. A comparative analysis of TSKF and available methods is performed on simulated and real chemical plant data. Root cause diagnosis of plant-wide oscillations, as a concrete example of data cleaning and knowledge discovery in the process data, is provided. Plant-wide oscillations can negatively influence the overall control performance of the process and the detection results are often affected by noise at different frequency ranges. To address such a problem, an information transfer method combining spectral envelope algorithm with spectral transfer entropy is proposed to detect and diagnose such oscillations within a specific frequency range, mitigating the effects from measurement noise. The feasibility and effectiveness of the proposed method are verified and compared with available methods through both simulated and industrial case studies.Chemical Engineerin

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Estimating Methane Emissions in Canada Using Atmospheric Observations from Earth to Space

Methane is a significant greenhouse gas with 25–32 times the global warming potential of carbon dioxide. Global sources and sinks of methane are understood to be 550 ± 60 Tg a-1. The possible causes of changing decadal trends in atmospheric methane concentrations since the 1990's is not well understood, since this requires a precision in global emissions quantification better than 20 Tg a-1. Atmospheric observations at the local, regional, or national scale can provide "top-down" constraints on emissions to verify "bottom-up" emissions that may not be well characterized. Cavity ring down spectroscopy (CRDS) instruments deliver highly precise in-situ measurements of methane, with 1 Hz precision better than 2 ppb. A comprehensive aircraft campaign in the Athabasca Oil Sands Region of Alberta (AOSR) in summer 2013, led by Environment and Climate Change Canada (ECCC), deployed a CRDS alongside a suite of instrumentation to measure atmospheric pollutants and meteorological parameters. These observations allowed for the comprehensive identification and quantification of methane emissions from unconventional oil extraction. Emissions estimates were 48% higher than those reported in the national greenhouse gas inventory. A series of lower cost follow up campaigns in 2014 and 2017 using a CRDS instrument mobilized with a vehicle allowed for cold season monitoring of emissions and select quantification where atmospheric parameters were favorable, showing continued discrepancies with inventory reporting. To estimate emissions across Canada at the national scale, methane measurements from ECCC long-term monitoring stations over 2010-2015 were utilized in conjunction with satellite remote sensing observations from the Greenhouse Gas Observing Satellite (GOSAT) operated by the Japanese Aerospace Agency (JAXA). These atmospheric observations were assimilated in the GEOS-Chem chemical transport model to constrain emissions using a Bayesian inverse modelling methodology. Results showed 42% higher emissions from anthropogenic sources and 21% lower emissions from natural sources, which are mostly wetlands, when compared to the prior estimate. Through the combinations of all studies presented herein, approximately 2–4 Tg a-1 of methane emissions in Canada were reallocated for the year of 2013, where 1–3 Tg a-1 was added to anthropogenic sources and 2–4 Tg a-1 was deducted from natural sources, which is substantial relative to the anthropogenic inventory in Canada which is 4–5 Tg a-1. This reallocation is 0.4–0.8% of the entire global budget of 550 Tg a-1, where only a ~3% change in the source-sink balance can cause the observed trends in atmospheric methane. These results show that atmospheric observations from surface, aircraft and satellites are critical for constraining the methane budget in Canada, and improvements are necessary to these types of atmospheric observations over the world to constrain the methane cycle within the precision needed to understand decadal trends

Risk Informed Support of Decision Making in Nuclear Power Plant Emergency Zoning - Generic Framework towards Harmonising NPP Emergency Planning Practices

The report provides a systematic overview of the essential aspects of risk informed support of decision making (RIDM) in nuclear power plant (NPP) emergency zoning (EZ) as a contribution to harmonising strategic practices in the area. Owing to the state-of-the-art understanding and increased characterisation of NPP severe accidents, overall management of them should be analysed as an integrated complex process. The interrelationship of NPP emergency operating procedures, safety and risk assessments, severe accident management guidelines, and emergency off-site actions should be planned and organised to minimize the consequences of such accidents. A deterministic approach, coupled with both probabilistic safety assessment (PSA) technology and PSA results can play significant role in the development of relevant nuclear utility, regulatory and all stakeholders policies. The report describes the background, objectives and current state of a corresponding activity within JRC-IE's Analysis and Management of Nuclear Accidents (AMA) Action on probabilistic safety / risk assessment methodologies and practices for RIDM approach applied to NPP EZ. The approach is interdisciplinary, based on integration of PSA technology, severe accident phenomenology, and radiological protection.JRC.F.4-Nuclear design safet