Matters to be addressed:

- What is sustainability?

- UN's Sustainable Development Goals

- Sustainability Drivers for Business

Abstract:

The production of biodiesel using lipases by transesterification of vegetable oils, has drawn great attention for being an efficient and sustainable process, This study is based on the development of a kinetic model that can describe the reactions and represent the phases involved in it by applying a thermodynamic model, UNIFAC.

Abstract:

Integration of the production of bioethanol and biodiesel in a biorefinery may improve the economic viability and environmental footprint of both processes. Microbial oil (MO), in turn, is seen as a promising source of triacylglycerol for biodiesel production. This work presents the techno-economic-environmental analysis (TEEA) of the integrated production of bioethanol, bioelectricity, and biodiesel in a sugarcane biorefinery, where MO from the yeast Rhodotorula toruloides feeds the biodiesel unit. The biorefinery produces 71.7 m3/h of bioethanol, 2.55 m3/h of biodiesel (that can replace 75.6% of the agricultural diesel demand), and 86.3 MW of surplus bioelectricity. Life cycle assessment of the integrated biorefinery had a lower footprint than the first-generation bioethanol plant when biodiesel was used in agricultural operations. The integrated process exhibits a positive economic performance, indicating that this is a feasible industrial option. Sensitivity analysis shows that R&D should mainly focus on bioreactor operation.

Abstract:

The current trends towards operational innovation and real-time decision making have posed new challenges and opportunities to process safety. Existing fault management approaches typically follow a reactive strategy, such as fault detection and diagnosis empowered by the recent impetus of data analytics. However, given the occurrence of a fault, instantaneous shutdowns of equipment and systems are required to prevent serious accidents. In this context, proactive fault management is of essential need which can inherently reduce the failure probability and consequence severity as well as to minimize the production losses due to unplanned shutdowns. In this talk, we introduce a risk-aware design and model predictive control optimization framework for fault prognosis and mitigation. The framework features: (i) dynamic risk assessment to quantify process risks as a multi-variate function of safety-critical design and operating variables, (ii) multi-parametric model predictive control with explicit consideration of risk propagation dynamics and upper limits, and (iii) (mixed-integer) dynamic optimization to integrate the multi-time-scale decision making to achieve optimal safety and economics performance. A real-world case study is used as showcase on methylcyclopentadienyl manganese tricarbonyl processing at T2 Laboratories.

Abstract:

This talk addresses two topics: (1) the development of a machine learning-based framework for process operability using surrogate model responses based on Kriging (also known as Gaussian Process Regression – GP); and (2) recent developments and challenges associated with a Python Toolbox for process operability calculations.

Currently, the available operability approaches for nonlinear systems are limited by the problem dimensionality that they can address, not being computationally tractable for high-dimensional systems. The proposed approach employs GP-based models to substitute the developed first-principles or process simulation-based models. The built surrogate models can generate responses that are comparable to the first- principles nonlinear models in terms of accuracy, while reducing the computational effort. To achieve this goal, a framework for the systematic analysis of highly nonlinear, large-dimensional systems at steady state is developed. The proposed approach is benchmarked against current operability methods and provides a new direction in the process operability field employing GP models. Case studies associated with natural/shale gas conversion are addressed to illustrate the effectiveness of the proposed methods.

Additionally, the current advances in implementing Process Operability algorithms in a comprehensive toolbox in Python will be discussed, including the validation of the toolbox calculations employing the membrane reactor system.