Simultaneous Validation of Online Analyzers and Process Simulators by Process Data Reconciliation

The increasing requirements for better quality with smaller variations are often difficult to fulfill in pulping plants. In chemi-thermo mechanical pulping (CTMP), the non-uniformity in the chemical treatment and the changes in the refiner's energy consumption often cause variations in pulp properties. The CTMP process has strong interactions between process variables and therefore necessitates decoupling of interactions in the refining control system. Together with the high variability in the process dynamics, lack of direct and reliable measurements makes it difficult to control the process. Consistency of the blow-line pulp is not always measured using an on-line sensor. If high consistency sensors are used, also redundant measurements or soft sensors may need to be developed for feedback control purposes. Pulp quality variables such as freeness and shive content are often analyzed infrequently and/or there is a long dead time and time constant between the refiner's blow line and the on-line analyzer's sensor. In this paper, pulp properties of CTMP refining are modeled and a new two-level quality control system is proposed. The nonlinear quality models are verified by simulation and compared with measurements from an existing on-line pulp quality analyzer. The two-level quality control system uses the process models for stabilizing the refiner conditions (pulp consistency and specific energy consumption) and for optimizing the process operation and the pulp quality (freeness). The stabilizing controllers are designed using multivariable Hammerstein and cascade control structures. The optimizing controller calculates the setpoint for the stabilizing controller by using the inverse of the nonlinear multivariable quality model. A simple scheme for adapting the freeness model based on the on-line measurements is presented. The performance of the control system is illustrated in simulations.

The presence of measurement bias and random noise significantly deteriorates the information quality of plant data. Data reconciliation techniques for steady‐state processes have been widely applied to processing industries to improve the accuracy and precision of the raw measurements. This paper develops an algorithm for simultaneous bias correction and data reconciliation for dynamic processes. The algorithm considers process model error as an important contributing factor in the estimation of the measurement bias and process state variables. It employs black‐box models for the process as would be done when phenomenological models are difficult or impractical to obtain. Simulation results of a distillation column demonstrated that this algorithm effectively compensates constant and non‐constant measurement biases yielding much improved reconciled values of process variables. It has computational advantages over previously proposed algorithms based on non‐linear dynamic data reconciliation because an analytical solution is available when using linear process models to approximate the process.

Neuro-fuzzy Soft Sensor Estimator for Benzene Toluene Distillation Column

Data Reconciliation for Stationary Multi-Phase Multi-Stream Metallurgical Processes

The distributed control system (DCS) is the most prevalent means of implementing batch and continuous control in the process industry. The DCS platform may also be effectively used for the evaluation and demonstration of innovative algorithms developed at a university. The integrated online advanced controls, diagnostics, data analytics, and process modeling capability of a DCS facilitate university research. This working environment also prepares students for careers in industry. The paper discusses the industrial standards adopted by the DCS that allow students and researchers to concentrate on the application of new control capabilities. External applications such as Matlab can be used with the DCS to explore control and process modeling. In this environment, the student and researchers may take advantage of preconfigured and automated features of the DCS. Process simulations may be integrated into the DCS to create a virtual plant capability that is capable of running faster than real-time. The DCS supports wireless and internet access to the virtual plant which increases ease of use in a lab or pilot plant. The DCS system enables rapid exploration, discovery, and prototyping of process modeling and control technologies in a university environment and deployment in industry.

The prelims comprise: Introduction Introductory Concepts for Validation of Plant Data Formulation Software Solution Integration in the Process Decision Chain Optimal Design of Measurement System An Example Conclusions

Commercial analyzers for measuring the aromatics in the Claus furnace exit gas are currently not available and this leads to sub-optimal energy efficiency and poses asset integrity concerns. To address this problem a high-fidelity model is developed to function as a real time analyzer. Objective of this work is to incorporate the soft sensor in the architecture of Real Time Optimizer (RTO) to monitor the presence of aromatics in the Claus furnace exit stream. The soft sensor is incorporated in the RTO server which provides the access to the plant operating data and the DCS (Distributed Control System). Soft sensor function in the RTO involves the following steps: Soft sensor accesses the plant data and collects the needful input data for simulation Simulation software available in the RTO executes the softs sensor model simulation and generates the aromatics composition data Aromatics composition data is written to the DCS interface as a soft measurement Operators monitor the aromatic composition and accordingly adjust the fuel gas firing Aromatic soft sensor is developed as a kinetic model, which is function of rate parameters of several key reactions of the Claus furnace. The kinetic model of the Claus furnace is incorporated in a process simulation model and catalytic convertors are simulated too. Model is validated with large plant data to show that model predicts furnace temperature within +/- 5% error and aromatics composition within +/- 5 ppm. Simulation analysis shows that the furnace temperature can be decreased by at least 5 °C while ensuring no BTEX slip. Such change in furnace temperature leads to a reduction in fuel gas flow by ~200 Nm3/h, which translates to a monetary benefit of 0.5 million $/yr. Deployment of the soft sensor is currently in progress through engagement with RTO licensor. To the best knowledge of authors, currently, there is no simulator in the market which can adequately model aromatics oxidation phenomena and predict the aromatic content in the furnace exit. This soft sensor being deployed is novel and first of its kind and expected to achieve a sustainable energy efficiency.

Integration of life cycle assessment software with tools for economic and sustainability analyses and process simulation for sustainable process design

Mathematical model equations and their corresponding solution methods for membrane gas permeation and pervaporation were developed for direct use in process simulators, without the need for external, custom programming. The models are based on the solution-diffusion mechanisms for species permeation through nonporous membranes. Cross-flow and counter flow models using the logarithmic-mean trans-membrane partial pressure were used to simulate spiral wound and hollow fiber membrane configurations. The Chen approximation of the log-mean partial is recommended for avoiding divergence in the iterative solution methods required to solve the non-linear model equations. The models incorporate temperature, pressure, composition, mass flow, membrane area, and species permeance effects into the simulations. The models were designed for implementation in process simulators using intrinsic capabilities for calculating material and energy balances and predicting physical and thermodynamic properties. Examples are given using the process simulator HYSYS.

The European Steel sector is committed to investigate solutions for reducing its environmental impact in terms of atmospheric and water emissions, energy consumption and solid waste production. To this aim, the project entitled Environmental Impact Evaluation and Effective Management of Resources in the EAF Steelmaking (EIRES), that is funded by the Research Fund for Coal and Steel, aims at providing electric steelworks with a tool for a systematic evaluation and investigation of solutions improving the process sustainability. As complex process modifications, before being practically implemented and tested on the field, need a thorough evaluation, a simulation model of the whole electric steelmaking route was developed as a part of EIRES tool. The paper presents such simulation model, which has been developed through Aspen Plus® V 8.6 in order to assess the impact of each operation unit, including both production processes and fumes and wastewater treatment plants. A case study is presented, which compares the environmental and energy impact related to the production of some of the most commonly produced steel grades. Correlations between the steel grade and the consumed electric energy or the slag amount and composition were highlighted, that can be a useful starting point for further assessment of potential margins of improvement of electric steelwork sustainability.

Objectives: This study aimed to identify the real-time word learning processing aspects of children in each group by classifying groups according to their actual vocabulary acquisition performance (offline processing data) in QUIL (Quick incidental learning). We compared whether there was a significant difference between the QUIL offline and online processing data of the two groups, and finally we attempted to explore whether QUIL offline and online processing data had a significant correlation with children’s working memory. Methods: Thirty-three children [21 with TD (Typically developing children); and 12 with SLI (Children with specific language impairment)] aged 3- to 6-year-old participated in this study. K-mean cluster analysis was conducted to create new groups based on QUIL offline scores, and to examine children’s online word learning processing. To analyze the children’s word learning process with an eye-tracker, the animations recorded with narration were shown to the children through a computer with an eye-tracker attached. Results: There was a significant difference between the two clusters at the third exposure condition in online data (fixation duration). In addition, there was a significant correlation between QUIL online processing and linguistic WM (Working memory) in cluster 1, and between QUIL offline scores and nonlinguistic WM in cluster 2. Conclusion: When new vocabulary is exposed for cluster 2, it can be inferred that the efficiency of vocabulary acquisition will be improved if visual information is intensively combined with language information.

Effect of redundancy on estimation accuracy in process data reconciliation

Thermodynamic investigation and environment impact assessment of hydrogen production from steam reforming of poultry tallow

In this work, we present a solution procedure for design of a chemical process for effectively adjusting calorific values in an offshore regasification terminal. To tackle the technical and commercial issue in the liquefied natural gas (LNG) industry caused by differences of LNG calorific values between importing countries, many methods and configurations are being studied. This design problem is defined in two parts: a generalized disjunctive programming (GDP) problem with one objective and a multiobjective problem for minimizing the operating costs and the performance of natural gas liquids (NGLs). First, the GDP problem has been mathematically reformulated as a mixed-integer nonlinear programming (MINLP) problems, and the MINLP technique incorporated into the process simulator using its own optimization capabilities has been suggested. For solving the resulting bicriterion problem with the MINLP problem, we have suggested the heuristic procedure that reduces the number of discrete solutions which are n...

The use of soft sensors for monitoring purposes is an established practice in the process industry. In this study, the focus has been on developing a soft sensor that can be used to monitor the product recovery rate of double-ended, high-purity distillation columns with a side-draw. To this end, this study specifically focuses on developing a cost-effective and accurate soft sensor for an industrial methanol distillation unit where a side-draw is used to meet the parts per million level impurity specifications. The novel soft sensor is based on the unique characteristics of the mass balance in this type of column, and uses the density measurement at the side-draw together with the flow rates of the side-draw and product draw to calculate the product recovery rate. The developed soft sensor was validated against real plant data as well as on a process simulation of an industrial methanol distillation column. The soft sensor demonstrated the ability to predict product recovery to an accuracy of 0.05% and sh...