Matteo Cicciotti

Operational optimization of networks of compressors considering condition-based maintenance

Abstract The paper presents a mixed integer linear programming model which deals with the optimal operation and maintenance of networks of compressors of chemical plants. This optimization model considers condition-based maintenance which involves the degradation of the condition of the compressors. The paper focuses on online and offline washing, two different cleaning procedures which reduce the extra power used by the compressors due to fouling. The state-of-the-art has demonstrated the optimal schedule of the maintenance of a single compressor neglecting the interactions between operation and maintenance of more than one compressor. The suggested optimization model studies a compressor station with multiple compressors and provides their optimal schedule and the best decisions for their washing. Different case scenarios examine the influence of different types of washing methods on the total costs of operation and maintenance. The paper demonstrates the benefits of the optimization and demonstrates that maintenance and operation have to be examined simultaneously and not separately, in contrast to common industrial practice and previous approaches in the literature.

Physical modelling of industrial multistage centrifugal compressors for monitoring and simulation

Currently, industrial applications to monitoring, simulation and optimization of compressors employ empirical models that are either data-driven or based on the manufacturer performance maps. This paper proposes the use of one-dimensional aerodynamic models for industrial applications such as simulation and monitoring. The physical model establishes causality relationships among input and output variables that are tuned to match the real compressor by using operation data. The application of the method is shown using data from an industrial multistage centrifugal compressor with interstage coolers and variable inlet guide vanes. This is a more complex but more relevant case study for process industry, as opposed to the single-stage variable speed compressors, which is the common example in the literature.

Operational Optimization of Compressors in Parallel Considering Condition-Based Maintenance

Abstract This paper suggests an optimization framework for the process and maintenance operations of a network of compressors. The health condition of a compressor varies during its operation due to mechanically degrading effects (e.g. fouling and corrosion) which results in decreasing performance and increasing power consumption. Currently, the industrial maintenance strategy considers preventive maintenance cycles, i.e. maximum running time of the compressors. Typically, the maintenance schedule of a compressor is examined separately without considering the interactions between the compressor and the overall process. In this work, the increase in the power consumption of each compressor is linearly correlated to the periods of continuous operation, and the results demonstrate that the simultaneous optimization of condition-based maintenance and operation reduces the overall costs.

Simultaneous Nonlinear Reconciliation and Update of Parameters for Online Use of First-Principles Models: An Industrial Case-Study on Compressors

Abstract Online uses of first-principles models include nonlinear model predictive control, softsensors, real-time optimization, and real-time process monitoring, among others. The industrial implementation of these applications needs accurate adaptive models and reconciled data. The simultaneous reconciliation and update of parameters of a first- principles model can be achieved using an optimization framework that exploits physical and analytical redundancy of information. This paper demonstrates this concept by means of an industrial case-study. The case-study is a multi-stage centrifugal compressor for which a first-principles model was recently developed. The update of the model parameters is necessary to capture slowly progressing mechanical degradation (e.g. due to fouling and erosion). The reconciliation of the data is necessary for reducing downtime of the online model-based applications caused by gross errors. Two industrial cases including sensor failures were analysed. Applying the proposed framework, it was possible to reconcile the measurements for both cases.

Online Performance Monitoring of Industrial Compressors Using Meanline Modelling

This paper proposes a framework for detecting mechanical degradation online and assessing its effect on the performance of industrial compressors. It consists of a model of the machine in undegraded condition and of a degradation adaptive model. The proposed methodology for online degradation detection differentiates itself from those found in the literature as the undegraded model is not linearized and ambient/inlet conditions are explicitly taken into account. The degradation is modelled through adaptive parameters which are estimated and updated online through the solution of a constrained minimization problem within a moving window. It uses available process measurements of flow, pressures, temperatures and composition. The update of the parameters guarantees the model accuracy and it permits the estimation of the effects of mechanical degradation away from the compressor running line.The performance monitoring framework has been successfully applied on an industrial air centrifugal compressor. It was found that after 3250 hours of operation from the previous maintenance the efficiency and the pressure ratio had dropped approximately 5.5% and 2.5% of their respective undegraded values. Furthermore, it was found that the performance deviations from the baseline depend from the position of the operative point in the performance map. In fact, the pressure ratio drop was lower (2%) and efficiency drop was higher (6%) for lower inlet guide vanes opening whereas pressure ratio drop was higher (3%) and efficiency drop was lower (1.6%) for higher inlet guide vane opening.© 2014 ASME

Systematic One Zone Meanline Modelling of Centrifugal Compressors for Industrial Online Applications

For developing model-based online applications such as condition monitoring and condition-based maintenance or real-time optimization, highly representative and yet simple physical models of centrifugal compressors are necessary. Previous investigations have shown that in this context meanline models represent a valid alternative to the commonly used empirical based modelling methodologies such as polynomial regression models or artificial neural networks.This paper provides a methodology for tailoring meanline models to multistage centrifugal compressors by appropriate selection and adaptation of loss correlations. Guidelines for the selection of the boundary conditions are also provided.The potential of the methodology is demonstrated in the Proof-of-concept section using two sets of data obtained from an air multistage centrifugal compressor operated in BASF SE, Ludwigshafen, Germany. The first set of data was used to calibrate the model whereas the second one was used for validation. The model results show that the predictions of stagnation temperature and pressure at the outlet of the stage deviate from the measurements respectively 0.15–3% and of 0.66–1.1% respectively. The results are discussed in the current paper.Copyright

Operational and Maintenance Planning of Compressors Networks in Air Separation Plants

In this chapter, a general optimizasion-based approach for the integrated operational and maintenance planning of compressor networks in air separation facilities is presented. The proposed mathematical programming model considers operating constraints for compressors, performance degradation for compressors, several types of maintenance policies and other managerial aspects. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments, the timing and the type of necessary maintenance tasks as well as the outlet mass flow rates for compressed air and distillation products are optimized. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. The proposed optimization model can be readily used within a rolling horizon scheme to deal with uncertainty. Several case studies of the air separation plant of BASF SE in Ludwigshafen are solved. The results clearly demonstrate the considerable energy and total cost savings due to the simultaneous planning of operational and maintenance tasks.

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.