Jose A. Romagnoli

Process synthesis and optimisation tools for environmental design: methodology and structure

Abstract Process design requires the simultaneous satisfaction of environmental, economic and social goals. This invariably requires some trade off between these objectives. The challenge for process design engineers is to develop synthesis and analysis tools, which support this requirement. Process System Engineering (PSE) techniques for multiple objective optimisation have to date focused typically on the optimisation of cost versus the potential for waste minimisation, with the recent inclusion of operability issues. The incorporation of environmental sensitivity into PSE approaches has been less than satisfactory. Much of this stems from the (seeming) difficulty in translating process information to environmental objectives. It is our argument that life cycle assessment (LCA), a methodology for quantifying the full ‘cradle-to-grave’ impact of industrial processes, can be used to assist in developing environmental objectives for process design and analysis. In this paper, we resrict our analysis to the multiple objective optimisation of environmental and economic objectives. Our approach is demonstrated for the case study of a nitric acid plant, modeled using Hysys

Application of Wiener model predictive control (WMPC) to a pH neutralization experiment

pH control is recognized as an industrially important, yet notoriously difficult control problem. Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are considered to be ideal for representing this and several other nonlinear processes. Wiener models require little more effort in development than a standard linear step-response model, yet offer superior characterization of systems with highly nonlinear gains. These models may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. In this paper, Wiener model predictive control (WMPC) is evaluated experimentally, and also compared with benchmark proportional integral derivative (PID) and linear MPC strategies, considering the effects of output constraints and modeling error.

Rectification of process measurement data in the presence of gross errors

Abstract A systematic strategy is developed for the location of the source and the rectification of gross, biased measurement errors in a chemical process. The proposed strategy proceeds in three levels: (a) A structural analysis of the balance equations identifies subsets of balances with measurements which are suspected to possess gross errors. (b) A sequential analysis of the balance equations with suspect measurements further reduces the size of the problem. Statistical criteria are used in this step. (c) Finally, a sequential analysis of the suspect measurements appearing in the reduced set of balances leads to the identification of the source of the gross errors. The proposed strategy: (i) reduces the size of the data reconciliation problem significantly, even for large-scale chemical processes, (ii) is computationally simple and (iii) it conforms with the general process of variable monitoring in a chemical plant. Numerical examples are presented to clarify the elements of the procedure involved and demonstrate their value and effectiveness in dealing with realistic situations.

On the rectification of measurement errors for complex chemical plants: Steady state analysis

Abstract An algorithmic approach is developed in the present work, which allows: (i) the classification of the measured and unmeasured process variables, (ii) The estimation of the desired unmeasured variables (using available measurements) in a complex chemical plant and (iii) the rectification of the process measurements. The method is very general and unlike the previous works it can be used in conjunction with linear and nonlinear mass and heat balances. The size of the estimation problem has been reduced significantly. Different algorithms are presented which permit the solution of special practical problems, and stochastic tests are proposed to check the consistency of the process data and detect gross measurement errors. Several examples demonstrate the developed algorithms and indicate the usefulness of the proposed approach, in classifying and estimating the processing variables and adjusting the process data through the utilization of mass and heat balances.

Process data de-noising using wavelet transform

The recovery of process information from noisy data de-noising is studied by investigating the classical solution of the estimation problem first. Next, the effectiveness of wavelet-based algorithms for data recovery is considered. A novel method based on coefficient de-noising according to WienerShrink method of wavelet thresholding is proposed. Simulation results are presented, highlighting the advantages of the de-noising method over the classical approaches based on the mean square error criterion.

A decision support tool for strategic planning of sustainable biorefineries

Abstract In this paper we formulate, implement, and test a model for technology and product portfolio design for a multi-product multi-platform biorefining enterprise. The model considered is an MILP financial planning model with the objective of maximizing the stakeholder value. Integer variables are used to select appropriate feedstocks, technologies, and products, material and capacity balances are used to design capacity and set production targets, while cash balances are used to describe investment and operations financing. Stakeholder value is described as the shareholder value with monetized environmental implications in terms of emissions mitigation costs and credits. Process integration schemes utilizing emissions are considered to reduce the emissions load and add to the bottom-line. A preliminary process design and product portfolio is provided as a result. Advantages of process integration are quantified using a central utilities facility and effluent recycles. Sensitivity analysis is conducted to determine important parameters that shape the objective function.

Use of orthogonal transformations in data classification-reconciliation

Abstract In this paper, the use of orthogonal factorizations, more precisely the Q-R decomposition, to analyze, decompose and solve the linear and bilinear data reconciliation problem is further investigated. It is shown that the decomposition provides additional insight in identifying structural singularities in the system topology, allowing the problem to decompose into lower dimension subproblems. Energy balances are explicitly considered. Two examples of application are presented.

Application of Wiener model predictive control (WMPC) to an industrial C2-splitter

Abstract Dual composition control of a high-purity distillation column is recognized as an industrially important, yet notoriously difficult control problem. It is proposed, however, that Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are ideal for representing this and several other nonlinear processes. They are relatively simple models requiring little more effort in development than a standard linear step response model, yet offer superior characterization of systems with highly nonlinear gains. Wiener models may be incorporated into MPC schemes in a unique way that effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC, especially in the analysis of stability. In this paper, Wiener model predictive control is applied to an industrial C2-splitter at the Orica Olefines plant with promising results.

Dynamic probabilistic model-based expert system for fault diagnosis

Abstract The design and implementation of a probabilistic model-based fault diagnosis expert system is described in this paper. Possible cause and effect graph (PCEG) methodology, an enhanced signed-directed graph (SDG) approach, was used for qualitative modeling. A rule-based approach is proposed to transform the Bayesian belief network into an acyclic network dynamically during the diagnosis phase to allow simple on-line probability calculation in a belief network with causality loops. A dynamic time-delay methodology is also proposed to manage the possibility of phantom alarms, which are the consequences of process time-delays. The application to a pilot scale distillation column with communication with DCS environment is presented. Two sample runs were included to demonstrate the concept of dynamic causal network modification and time-delay management.

Robust characteristic polynomial assignment

In this paper a computational method for designing controllers which attempt to place the characteristic polynomial of an uncertain system inside some prescribed region is presented. An objective function consisting on two terms is proposed, penalizing both the distance to a given controller and the size of the uncertainty region developed to solve the robust assignment problem. The algorithm is based on semi-infinite programming theory, and the special structure of the problem is exploited to get both simpler methods and convergence proof. The way in which the uncertain parameters affect the plant coefficients is realistic, since it includes multinomial dependence. An example of application is given to illustrate the approach.