Mar Pérez-Fortes

Advanced simulation environment for clean power production in IGCC plants

Oxygen-blown biomass integrated gasification combined cycle (IGCC) plants are one of the most promising options for clean energy generation with CO2 abatement potential. However, the integrated nature of IGCC leads to difficult design problems. In this study, we present an advanced simulation environment for the preliminary design and retrofit of IGCC plants. We describe the modelling approach, the model validation strategy and the plant behaviour, as determined by sensitivity analyses. The simulation environment uses Pareto curves to examine various co-gasification and co-production case studies in terms of technical, economic and environmental performance. It serves as a decision support tool in the design stage, which can be used to explore ways to improve plant performance and to analyse the influence of raw materials and the unit’s operational parameters. The test and validation results are discussed.

Raw Materials Supply

The pressure on reducing environmental footprint is facilitating the emergence of energy supply chains that have biomass as main feedstock. For the development of efficient energy supply chains from biomass it is required to properly integrate the various elements that comprise such systems (e.g., biomass supply, pre-treatment facilities and technologies for biomass to energy and/or fuels conversion). Additionally, it is recognised that a concerted effort is required, embracing the different supply chain entities, in order to correctly estimate environmental burdens and to propose effective environmental strategies. This chapter proposes the use of a mathematical modelling approach as an analytical tool that can support decision-making towards accomplishing the design and planning of efficient multiple source—multiple product bio-energy supply chains. The mathematical formulation of this problem becomes a multi-objective MILP (moMILP). Criteria selected for the objective function are the net present value (NPV) and the overall environmental impact, which is computed using the Impact 2002+ indicator. The main advantages of this approach are highlighted through a case study of a biomass-based supply chain that comprises different components geographically distributed over Spain. For comparison purposes, such a supply chain is contrasted to one embracing coal usage.

Enhanced modeling and integrated simulation of gasification and purification gas units targeted to clean power production

This work presents a structured and validated conceptual model of an integrated gasification combined cycle (IGCC) power plant. A pressurized entrained flow (PRENFLO) gasifier, subsequent gas cleaning operations for fly ashes, ammonia, and sulfur compounds removal, heat recovery steam generator (HRSG) and combined cycle unit operations have been modeled in steady state. The model has been developed using Aspen Hysys® and Aspen Plus®. Parts of it have been developed in Matlab, which is mainly used for artificial neural network (ANN) training and parameters estimation Predicted results of clean gas composition and generated power present a good agreement with industrial data. This study is aimed at obtaining a support tool for optimal solutions assessment of different gasification plant configurations, under different input data sets.

Strategic Planning of Biomass Supply Chain Networks for Co-combustion Plants

Abstract An optimization-based supply chain design-planning approach is applied to process seasonal and highly distributed biomass waste to fulfill the demand of an existing park of coal power plants. Economic and environmental criteria are integrated in the resulting mixed integer linear program mathematical model: the net present value, measured in €2010, and life cycle assessment (LCA) through the Impact 2002+ method. Input data comprise suppliers geographically distributed and facility locations, the alternatives for technological equipment (e.g., pretreatment technologies), characterization of biomass properties, and models granularity. The model suggests the location and connectivity between providers and consumers, and pretreatment units capacities. The size of the optimization problem requires large computational time. To reduce the calculation time, a novel Lagrangian decomposition method is applied. A large retrofit case study is solved using the aforementioned decomposition scheme to demonstrate the capabilities of the proposed approach.

Modelling Syngas Generation

Syngas generation refers to the production of a synthetic or synthesis gas that is mainly composed of CO and H2, in different proportions according to the generation process used and the raw material composition. Gasification is the referred technique to produce syngas. It can be used for different purposes, such as power and/or heat generation or for chemicals and fuels production. This chapter describes, we comment the generalities of syngas and its main characteristics and properties, also discuss its possible sources and focus on biomass waste and its co-gasification with coal and petcoke. Then, gasification modelling most common approaches are mentioned. A thermochemical equilibrium model is presented here as the model used for gasification plant conceptual design. Through sensitivity analysis technique, the effects of the reactor temperature and pressure are seen in syngas composition. This chapter enumerates the major hypothesis assumed in this syngas generation step, which must be inevitably applied in modelling and optimizing the entire gasification plant.

Forecasting CO2 emissions due to gasifier degradation by time-series analysis

There is a growing interest in the use of gasification to contribute to the energy share, as a more efficient alternative to fossil fuels combustion, especially with coal and biomass in large power plants. In order to unveil the effect of time in the degradation of the gasifier operation and the influence that operating conditions have over reactor performance, the evolution of controlled and observed variables should be studied. Two statistical techniques are used in the present work: (i) multiple regression analysis, which provides information about the relationship between several predictor variables and a dependent variable, and (ii) time series analysis, which may allow identifying the nature of the phenomenon associated with reactor degradation as a function of time. The final goal consists of evaluating and predicting the reactor natural instability by monitoring CO2 emissions. Results demonstrate the capability of forecasting techniques to predict reactor degradation with time. Thus, the tendencies of the variables and parameters in the gasifier may be better determined to avoid faulty operation.

Global Clean Gas Process Synthesis and Optimisation

This chapter begins with an introduction to the different possible metrics related to clean gas process synthesis and its subsequent usage. Latter, the different techniques for tackling with multiple criteria are presented, emphasising the use of multi-criteria decision analysis (MCDA) and multi-objective optimisation (MOO). The different criteria elected here for optimisation are described and later used as key performance indicators (KPI) for the proposed scenarios, in chapter “ Selection of Best Designs for Specific Applications”. Finally, a case study related to the operation of an IGCC plant considering coal–petcoke or natural gas as a fuel is assessed applying the optimization concepts introduced here and taking into account the operation considerations developed in this and in previous chapters.

Optimal bio-based supply chain with carbon capture and use: An economic and environmental approach

Abstract In this work, an optimisation model for the optimal design and planning of energy supply chain is presented. The model intends to fulfil specific demands of electricity and methanol under a stipulated CO2 emissions reduction. The electricity supplied to the methanol plant is produced by the co-combustion of biomass (renewable). We consider that incorporating CO2 capture technologies causes a reduction in the efficiency of cocombustion plants. Different scenarios are investigated to evaluate the conditions under which this type of projects may become financially feasible. A case study based on the Spanish energy grid is utilised for this exercise.

Selection of Best Designs for Specific Applications

The present chapter assembles all the previous chapter’s concepts and methodologies with the aim of selecting a given process design. The developed superstructure is used to simulate scenarios with different feedstocks and process topologies for co-production of electricity and hydrogen. The most representative output data are shown, and the described points of view, discussed in chapter “ Global Clean Gas Process Synthesis and Optimisation”, are here evaluated under a techno-economic and environmental assessment. Sixteen scenarios are considered encompassing four different feedstocks combined with four different plant topologies; electricity generation with syngas, electricity generation with hydrogen, hydrogen production and hydrogen production with PSA flue gas profit in the CC.

Examples of Industrial Applications

The description of the 335 MWeISO coal-based Puertollano IGCC power plant as example of industrial application of IGCC technology together with its main lessons learnt are summarised in this chapter. This chapter includes process description, syngas analysis, main operational real data (power production and emissions), and main causes of unavailability as well as R&D lines. These are mainly focused on improvement of IGCC technology taking into account efficiency increase and emissions reduction. So, first results of the CO2 capture and H2 co-production 14MWth pilot plant installed in the Puertollano IGCC are included.