Fabrizio Bezzo

Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries

Abstract Climate change mitigation has become a binding driver in biofuels production. First generation bioethanol, initially indicated as the most competitive option, is now incurring in ever increasing discredits forcing the transition towards more sustainable productions (i.e. second and third generation technologies). This paper addresses the strategic design and planning of corn grain- and stover-based bioethanol supply chains through first and second generation technologies. A Mixed Integer Linear Programming framework is proposed to optimise the environmental and financial performances simultaneously. Multi-period, multi-echelon and spatially explicit features are embodied within the formulation to steer decisions and investments through a global approach. A demonstrative case study is proposed involving the future Italian biomass-based ethanol production. Results show the effectiveness of the optimisation tool at providing decision makers with a quantitative analysis assessing the economic and environmental performance of different design configuration and their effect in terms of technologies, plant sizes and location, and raw materials.

A comprehensive approach to the design of ethanol supply chains including carbon trading effects

The optimal design of biofuels production systems is a key component in the analysis of the environmental and economic performance of new sustainable transport systems. In this paper a general mixed integer linear programming modelling framework is developed to assess the design and planning of a multi-period and multi-echelon bioethanol upstream supply chain under market uncertainty. The optimisation design process of biofuels production systems aims at selecting the best biomass and technologies options among several alternatives according to economic and environmental (global warming potential) performance. A key feature in the proposed approach is the acknowledgement of an economic value to the overall GHG emissions, which is implemented through an emissions allowances trading scheme. The future Italian biomass-based ethanol production is adopted as a case study. Results show the effectiveness of the model as a decision making-tool to steer long-term decisions and investments.

A general methodology for hybrid multizonal/CFD models: Part I. Theoretical framework

Multizonal models have been widely used for modelling the effects of mixing non-idealities in process equipment, presenting a realistic trade-off of computational efficiency and predictive accuracy between simple models based on idealised descriptions of mixing and full computational fluid dynamics (CFD) computations. However, a key weakness of multizonal models has been the difficulty of characterisation of the flow-rates between adjacent zones, and also of fluid mechanical quantities, such as the turbulent energy dissipation rate, that have important effects on the process behaviour within each zone. This paper presents a formal framework for addressing the above difficulties via a multiscale modelling approach based on hybrid multizonal/CFD models. The framework is applicable to systems where the fluid dynamics operate on a much faster time-scale than other phenomena, and can be described in terms of steady-state CFD computations involving a (pseudo) homogeneous fluid, the physical properties of which are relatively weak functions of intensive properties. Such processes include crystallisation and a wide variety of liquid-phase chemical and biological reactions.

A general framework for the integration of computational fluid dynamics and process simulation

Abstract Computational fluid dynamics (CFD) and process simulation are widely used in the process industry. The two technologies are largely complementary, each being able to capture and analyse some of the important process characteristics. Their combined application can, therefore, lead to significant industrial benefits. This is especially true for systems, such as chemical reactors, in which steady-state performance, dynamics and control strategy depend on mixing and fluid flow behaviour. This paper presents a new approach for the integration of the capabilities of CFD technology and process simulation via a general interface that allows the automatic exchange of critical variables between the two packages, leading to a simultaneous solution of the overall problem. The approach applies to both steady-state and dynamic problems. The feasibility of the approach and its first practical implementation are demonstrated by integrating a widely used CFD package (Fluent 4.5, by Fluent Inc.) within a general-purpose advanced process simulator (gPROMS 1.7, by Process Systems Enterprise Ltd. (1999)). One case study involving a batch reactor is used to illustrate the ability of the combined tool to provide information on the detailed interactions between fluid mechanics, heat transfer, reaction and control strategy, and to provide insights on important design and operational decisions.

Capacity planning and financial optimization of the bioethanol supply chain under price uncertainty

This work addresses the development of a dynamic spatially explicit MILP (Mixed Integer Linear Programming) modeling framework devised to optimize the design and planning of biomass-based fuel supply networks according to financial criteria and accounting for uncertainty on market conditions. The model capabilities in steering strategic decisions are assessed through a real-world case study related to the emerging corn-based bioethanol production system in Northern Italy. Two optimization criteria are considered, based on a risk-seeking or, alternatively, on a risk-adverse-approach.

A general methodology for hybrid multizonal/CFD models: Part II. Automatic zoning

Abstract Multizonal models and, in particular hybrid multizonal/CFD models, represent a powerful approach to process simulation when complex physical and chemical phenomena (e.g. crystallisation, polymerisation, bioreactions) need describing by taking into account mixing and other fluid flow properties. A major issue in setting up such models is the definition of a suitable network of zones. This paper addresses this issue by delivering some criteria to establish a suitable network of zones. The suggested procedure will be compared by means of a mixing example.

A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models.

The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK–PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK–PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.

Strategic optimisation of biomass-based energy supply chains for sustainable mobility

Abstract The identification of alternative and sustainable energy sources has been one of the fundamental research goals of the last two decades, and the transport sector plays a key role in this challenge. Electric cars and biofuel fed vehicles may contribute to tackle this formidable issue. According to this perspective, a multi-echelon supply chain is here investigated considering biomass cultivation, transport, conversion into bioethanol or bioelectricity, distribution, and final usage in alternative bifuel (ethanol and petrol) and electric vehicles. Multiperiod and spatially explicit features are introduced in a Mixed Integer Linear Programming (MILP) modelling framework where economic (in terms of Net Present Value) and environmental (in terms of Greenhouse Gases emissions) objectives are simultaneously taken into account. The first and second generation bioethanol production supply chain is matched with a biopower production supply chain assessing multiple technologies. Both corn grain and stover are considered as biomass sources. In the environmental analysis, the impact on emissions caused by indirect Land Use Change (iLUC) effects is also assessed. Results will show the efficacy of the methodology at providing stakeholders with a quantitative tool to optimise the economic and environmental performance of different supply chain configurations.

A model of chlorophyll fluorescence in microalgae integrating photoproduction, photoinhibition and photoregulation

This paper presents a mathematical model capable of quantitative prediction of the state of the photosynthetic apparatus of microalgae in terms of their open, closed and damaged reaction centers under variable light conditions. This model combines the processes of photoproduction and photoinhibition in the Han model with a novel mathematical representation of photoprotective mechanisms, including qE-quenching and qI-quenching. For calibration and validation purposes, the model can be used to simulate fluorescence fluxes, such as those measured in PAM fluorometry, as well as classical fluorescence indexes. A calibration is carried out for the microalga Nannochloropsis gaditana, whereby 9 out of the 13 model parameters are estimated with good statistical significance using the realized, minimal and maximal fluorescence fluxes measured from a typical PAM protocol. The model is further validated by considering a more challenging PAM protocol alternating periods of intense light and dark, showing a good ability to provide quantitative predictions of the fluorescence fluxes even though it was calibrated for a different and somewhat simpler PAM protocol. A promising application of the model is for the prediction of PI-response curves based on PAM fluorometry, together with the long-term prospect of combining it with hydrodynamic and light attenuation models for high-fidelity simulation and optimization of full-scale microalgae production systems.

Using Process Simulators for Steady-State and Dynamic Plant Analysis: An Industrial Case Study

Process simulation tools are widely adopted for the design and optimization of chemical processes. However, for quite a long time their use has been confined within research centres and highly specialized technical groups. This is especially true for dynamic simulation software, long regarded as a very specific tool requiring considerable expertise. In this work we intend to demonstrate the benefits that process engineers working on the plant may receive from an appropriate use of commercial software currently available for steady-state and dynamic simulation. A case-study concerning the purification section of an industrial plant for vinyl chloride monomer production will be considered. First of all, a steady-state simulation will be considered. Primarily, the simulation will allow a better judgement of the plant operating conditions; then it will be illustrated that sensitivity studies may produce great benefits in the general economy and productivity of the plant. Secondly, it will be shown how a dynamic model suitable for practical needs can be derived from the steady-state model. This model can be used as a powerful tool to assess the performance of the control system in handling standard operational disturbances as well as abnormal events. Simple improvements of the control system design will be also simulated and commented on.