George P.E. Brownbridge

The future viability of algae-derived biodiesel under economic and technical uncertainties

This study presents a techno-economic assessment of algae-derived biodiesel under economic and technical uncertainties associated with the development of algal biorefineries. A global sensitivity analysis was performed using a High Dimensional Model Representation (HDMR) method. It was found that, considering reasonable ranges over which each parameter can vary, the sensitivity of the biodiesel production cost to the key input parameters decreases in the following order: algae oil content>algae annual productivity per unit area>plant production capacity>carbon price increase rate. It was also found that the Return on Investment (ROI) is highly sensitive to the algae oil content, and to a lesser extent to the algae annual productivity, crude oil price and price increase rate, plant production capacity, and carbon price increase rate. For a large scale plant (100,000 tonnes of biodiesel per year) the production cost of biodiesel is likely to be £0.8-1.6 per kg.

Microkinetic Modeling of the Fischer–Tropsch Synthesis over Cobalt Catalysts

We present a detailed microkinetic analysis of the Fischer–Tropsch synthesis on a Co/γ‐Al2O3 catalyst over the full range of syngas conversions. The experiments were performed in a Carberry spinning basket batch reactor at initial H2/CO ratios between 1.8 and 2.9, temperatures of 469 and 484 K, and initial pressures of 2 MPa. A reaction mechanism based on the H2‐assisted CO activation pathway, which comprises 128 elementary reactions with 85 free parameters, was proposed to explain the experimental results. Each of these elementary reactions belongs to one of the following reaction groups: adsorption–desorption, monomer formation, chain growth, hydrogenation–hydrogen abstraction, or water–gas shift. A two‐stage parameter estimation method, based on a quasi‐random global search followed by a gradient‐free local optimization, has been used to calculate the values of pre‐exponential factors and activation energies. The use of data obtained from batch experiments enabled an effective analysis of dominating reactions at different stages of syngas conversions.

Simulation and life cycle assessment of algae gasification process in dual fluidized bed gasifiers

We present simulation results for the production of algae-derived syngas using dual fluidized bed (DFB) gasifiers. A global sensitivity analysis was performed to determine the impact of key input parameters (i.e. algae composition, gasification temperature, feed water content, steam-to-biomass ratio, and fuel-air equivalence ratio) on the product yields. The algae oil content was varied from 0 to 40 wt% to account for different algae strains and varying extents of oil extraction prior to the gasification process. It was found that the lower heating value (LHV) of syngas, typically ranging from 15 to 22 MJ kgalgae−1, is heavily dependent on the algae oil content. The cold gas efficiency (CGE) of the process varies over a range of 75 to 90%, depending primarily on the feedstock water content and steam-to-biomass ratio. A cradle-to-grave life cycle assessment indicated that the carbon footprint of syngas produced from algae feedstocks with 20 to 40 wt% oil fraction that is dried by a gas-fired dryer lies within a range of 70 to 195 g CO2 MJ−1. However, decarbonization of the drying stage via utilization of solar energy reduce the carbon footprint to values below 40 g CO2 MJ−1, which would compare favorably with the carbon footprint of syngas produced via steam reforming of natural gas (i.e. ∼100 g CO2 MJ−1).

Automated IC Engine Model Development with Uncertainty Propagation

This paper describes the development of a novel data model for storing and sharing data obtained from engine experiments, it then outlines a methodology for automatic model development and applies it to a state-of-the-art engine combustion model (including chemical kinetics) to reduce corresponding model parameter uncertainties with respect engine experiments. These challenges are met by adopting the latest developments in the semantic web to create a shared data model resource for the IC engine development community. The relevant data can be extracted and then used to set-up simulations for parameter estimation by passing it to the relevant application models. A methodology for incorporating experimental and model uncertainties into the model optimization procedure is presented. Data from seven operating points have been extracted from the proposed data model and have been incorporated into a state-of-the-art in-cylinder IC engine model through the optimization of model parameters whilst accounting for the model parameter and experimental uncertainties.

Parameterisation of a biodiesel plant process flow sheet model

Abstract This paper presents results of parameterisation of typical input–output relations within process flow sheet of a biodiesel plant and assesses parameterisation accuracy. A variety of scenarios were considered: 1, 2, 6 and 11 input variables (such as feed flow rate or a heaters operating temperature) were changed simultaneously, 3 domain sizes of the input variables were considered and 2 different surrogates (polynomial and high dimensional model representation (HDMR) fitting) were used. All considered outputs were heat duties of equipment within the plant. All surrogate models achieved at least a reasonable fit regardless of the domain size and number of dimensions. Global sensitivity analysis with respect to 11 inputs indicated that only 4 or fewer inputs had significant influence on any one output. Interaction terms showed only minor effects in all of the cases.

Evaluating smart sampling for constructing multidimensional surrogate models

Abstract In this article, we extensively evaluate the smart sampling algorithm (SSA) developed by Garud et al. (2017a) for constructing multidimensional surrogate models. Our numerical evaluation shows that SSA outperforms Sobol sampling (QS) for polynomial and kriging surrogates on a diverse test bed of 13 functions. Furthermore, we compare the robustness of SSA against QS by evaluating them over ranges of domain dimensions and edge length/s. SSA shows consistently better performance than QS making it viable for a broad spectrum of applications. Besides this, we show that SSA performs very well compared to the existing adaptive techniques, especially for the high dimensional case. Finally, we demonstrate the practicality of SSA by employing it for three case studies. Overall, SSA is a promising approach for constructing multidimensional surrogates at significantly reduced computational cost.