Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Operational optimization of networks of compressors considering condition-based maintenance

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

Backstepping methodology to troubleshoot plant-wide batch processes in data-rich industrial environments

Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity

Demand-side management and optimal operation of industrial electricity consumers: An example of an energy-intensive chemical plant

Concerns about the reliability of electricity supplies have motivated researches to investigate the possibility of electrical consumers to take a more active role in the operations of the power system. The work in this paper looks into the potential of an industrial chemical plant to provide support to the electricity grid by means of demand-side response (DR) programs. To do so, this paper proposes a method to assess the flexibility of the plant to provide electrical power reserves while ensuring that the production demand is satisfied, as well as an economic analysis of the plant operations incorporating DR programs to quantify the incentives the plant should receive in order to participate in these programs. Therefore, the current study presents a novel optimization framework which integrates production scheduling with DR programs, with the aim to determine optimal decisions for the operating conditions within the plant while safely providing services to the electricity grid

Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants - An industrial case study

The aim of this paper is to present a methodology for optimizing the operation of compressors in parallel in process industries. Compressors in parallel can be found in many applications for example in compressor stations conveying gas through long pipelines and in chemical plants in which compressors supply raw or processed materials to downstream processes. The current work presents an optimization framework for compressor stations which describe integration of a short term and a long term optimization approach. The short-term part of the framework suggests the best distribution of the load of the compressors (where the time scale is minutes) and the long-term optimization provides the scheduling of the compressors for large time periods (where the time scale is days). The paper focuses on the short-term optimization and presents a Real Time Optimization (RTO) framework which exploits process data in steady-state operation to develop regression models of compressors. An optimization model employs the updated steady-state models to estimate the best distribution of the load of the compressors to reduce power consumption and therefore operational costs. The paper demonstrates the application of the RTO to a network of parallel industrial multi-stage centrifugal compressors, part of a chemical process in BASF SE, Germany. The results from the RTO application showed a reduction in power consumption compared to operation with equal load split strategy

Atmospheric pollution and air ionization in two areas of central Italy [Inquinamento atmosferico e ionizzazione in due siti dell'Italia centrale]

In this paper, anthropogenic contributions to indoor and outdoor environmental pollution are investigated by evaluating the impact of each pollutant in terms of its potential capability to cause chronic diseases, such as respiratory and metabolic disorders and cancer. For this study, pollution data from two sites, Rome and Pietracupa, are compared and reveal substantially different characteristics

New “CAPE” solutions for olefins plants. Detailed dynamic simulation and dynamic real-time optimization

This paper deals with certain novel and appealing computer-aided process engineering (CAPE) solutions to improve flexibility, controllability, and operability of olefins plants. It shows the use of detailed kinetic schemes, developed and validated by Professor Sauro Pierucci and his colleagues at the Chemical Engineering group at Politecnico di Milano, to produce new effective tools for reliable and accurate dynamic simulation and dynamic real-time optimization methodologies. Preliminary results and tangible benefits are explained for a steam cracking furnace