Boris Kalitventzeff

process integration: Selection of the optimal utility system

Satisfying the energy requirements of a process at minimum cost is a key issue of the energy integration studies. Starting from the definition of the Minimum Energy Requirement of a process, we propose a method to compute the optimal utility system to satisfy the MER at minimum cost. The method uses three steps, the first step uses a generic utility system superstructure to identify what are the technology requirements of the process, i.e. the technologies that have to be used to satisfy the energy requirements. The model of the superstructure is based on the Effect Modelling and Optimisation (EMO) concepts that use a MILP (Mixed Integer Linear Programming) method to identify the best solutions. The second step is using an expert system to identify the available technologies able to satisfy the technology requirements identified in step 1, for example to identify the gas turbine (technology) that delivers a given heat load (requirement). The third step aims at targeting the optimal process configuration, i.e. to compute the integration of the different technologies to identify their best combinations. This step uses the EMO model to extract the best solutions. Multiple solutions are generated in order to compare the process configurations and identify the break even points of the technologies.

Targeting the integration of multi-period utility systems for site scale process integration

Abstract A method to target the optimal integration of the utility system of a chemical production site for multi-period operation has been developed. This paper is organised in three parts. After introducing the problem to be solved, the first part reports about the mathematical model used to solve the integration problem when the production specifications of the processes in the industrial site are constant (here average production levels). In the second part of the paper, the multi-period utility requirements of the chemical processes are analysed. An optimisation problem is stated to compute the multi-period load scenario defining the utility requirements with a minimum number of operating periods sets to satisfactorily covering the yearly operation of the total site. A genetic algorithm is used to solve this problem. Furthermore, the generic mathematical formulation of the multi-period utility targeting is provided, including the possibility to deal with different sets of technologies for the synthesis of the utility system. The third part of this communication presents the results obtained for a realistic problem described in the introduction. Discussion of the limits and of the powerful features of the method developed is shortly presented.

Identification of the optimal pressure levels in steam networks using integrated combined heat and power method

The integration of the utility system with all the plants of an industrial site is a mamor task for targeting the Minimum Cost of the Energy Requirement (MCER). Defining the target in terms of cost rather than in terms of energy introduces the problem of integrating the combined heat and power systems and especially the steam network. Furthermore, the integration of the utility system defines the complete list of streams that has to be considered for the Heat Exchangers Network (HEN) design or retrofit. In this paper, we present a method for computing the Integrated Combined Heat and Power (ICHP) target of industrial processes. The approach is based on the analysis of the shape of the balanced Grand Composite Curve of the process and the utilities. The ICHP target is an improvement of the rules for the appropriate placement of the combined heat and power engines. The method proposed, based on the application of the Carnot cycle, allows estimation of the combined power production potential of the system and identification of the optimal pressure levels of the steam network. From these results, the Minimum Cost of Energy Requirement target is computed by considering a model (presented elsewhere) for the integration of the steam network. Using a simplified example which originated from an industrial application, we show the benefits in terms of energy costs that might be obtained from the ICHP approach and its incidence on the rules for energy efficiency optimization of industrial processes.

Targeting the minimum cost of energy requirements: A new graphical technique for evaluating the integration of utility systems

The selection and the optimal integration of the utility system is a major step of any energy integration study. It allows to determine an energy target based on the cost of the utilities rather than on energy. As a result, we obtain the final list of streams that have to be considered for the Heat Exchanger Network (HEN) synthesis. The technique proposed for calculating the optimal integration of the utilities combines pinch analysis and mathematical techniques. For analyzing the numerical results obtained by the optimization, we propose a new definition of the composite curves (the integrated composite curves) that allows to evaluate the integration of the utility system, including the combined heat and power aspects. The new representation is illustrated on industrial examples. The applications show that it is a powerful tool for understanding the targeting of complex utility systems and for improving their integration to a process or an industrial site.

Energy integration of industrial sites: tools, methodology and application

One of the most important conclusions of the energy integration techniques is that energy efficiency can only be analyzed with respect to the pinch point location of the system. Therefore, the problem boundaries cannot be limited to the chemical process level but should be extended at the site scale level. In this paper, we discuss how to adapt the pinch based techniques in order to solve the industrial site integration. In the proposed approach, each process requirement is defined by its respective hot and cold composite curves that become hot and cold streams for the site scale problem. The use of mathematical programming techniques allows us to target the minimum cost of energy requirement (MCER) of the total site, including the integration of the combined production of heat and power by the steam network. The illustrative example shows an energy saving of 35% of the total site energy consumption with a very small pay back time.

Sensitivity calculations and variance analysis in plant measurement reconciliation

Analysis of the results of a data reconciliation program is made easier by extracting more information from the Jacobian matrix of the constraint equations. Standard deviation for all state variables (measured or not measured) is related to the standard deviation of measurements. Distinction between variables that are actually corrected by the validation process, and those that are merely derived from a single measurement is straightforward. Based on this information, decisions can be taken: deletion of unnecessary measurements, addition of new measurement points and their optimal selection, or identification of key measurements for which any enhancement of accuracy would result in significant improvement in the quality of the process validation.

Targeting the optimal integration of steam networks: Mathematical tools and methodology

Abstract Targeting the integration of the utility system in an industrial site, introduces the problem of combined heat and power production and more specifically the optimal integration of the steam network. We present a mathematical tool that allows, using only the definition of the headers of the system, to target the integration of the steam network to satisfy the heat requirements at minimum cost while maximising the combined production of mechanical power. This tool allows to automatically adapt the steam flowrates when the hot utility is changed. It defines the optimal steam flowrates to be considered in the Heat Exchanger Network design of the processes or of the industrial site. Furthermore this technique may be used to compute the optimal configuration of power plants.

Plant monitoring and fault detection: Synergy between data reconciliation and principal component analysis

Data reconciliation and principal component analysis are two recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). Principal component analysis can then be applied to raw process data for monitoring purpose. The combined use of these techniques aims at a better efficiency in fault detection. It relies mainly in a lower number of components to monitor. The method is applied to a modelled ammonia synthesis loop.

Modelling and optimization aspects in energy management and plant operation with variable energy demands-application to industrial problems

While formal methods to account for uncertainty in process optimization have been developed in the literature, little use of this research work has been reported for industrial applications. An attempt is made in this paper to integrate techniques for modelling and optimization under uncertainty in order to explore flexible operating scenaria and energy management schemes of real industrial utility systems. Multiperiod optimization principles are employed to account for variable demands and/or uncertain operating conditions, while discrete operating decisions (e.g. switching on/off a piece of equipment) are explicitly considered, enhancing, thus, the concept of operating cost for flexible operation.

Computer-Aided Design of Redundant Sensor Networks

Abstract A systematic method to design sensor networks able to identify key process parameters with a required precision at a minimal cost is presented. The procedure is based on a linearised model, derived automatically from a rigorous non-linear data reconciliation model. A genetic algorithm is used to select the sensor types and locations.