No abstract

No abstract

After just more than 100 years of history of industrial acetone–butanol–ethanol (ABE) fermentation, patented by Weizmann in the UK in 1915, butanol is again today considered a promising biofuel alternative based on several advantages compared to the more established biofuels ethanol and methanol. Large-scale fermentative production of butanol, however, still suffers from high substrate cost and low product titers and selectivity. There have been great advances the last decades to tackle these problems. However, understanding the fermentation process variables and their interconnectedness with a holistic view of the current scientific state-of-the-art is lacking to a great extent. To illustrate the benefits of such a comprehensive approach, we have developed a dataset by collecting data from 175 fermentations of lignocellulosic biomass and mixed sugars to produce butanol that reported during the past three decades of scientific literature and performed an exploratory data analysis to map current trends and bottlenecks. This review presents the results of this exploratory data analysis as well as main features of fermentative butanol production from lignocellulosic biomass with a focus on performance indicators as a useful tool to guide further research and development in the field towards more profitable butanol manufacturing for biofuel applications in the future. Electronic supplementary material The online version of this article (10.1186/s13068-019-1508-6) contains supplementary material, which is available to authorized users.

Given a sparse set of differential and algebraic equations (DAEs), it is always recommended to exploit the structure of the system’s sparsity (e.g., tridiagonal blocks matrix, band matrix, and staircase matrix, etc.), thus to use tailored numerical solvers in order to reduce the computation time. Very frequently, though, while highly structured, a couple of elements enter the description which make it difficult for the solvers to reach a solution. They are common in process control applications, where the states added to the plant description by the integral parts of the controllers introduce unstructured elements in the otherwise very structured Jacobian of the mathematical model. Such systems are characterized by a partially structured Jacobian, which inhibits the use of the solvers tailored to fit problems with fully structured matrices. In such cases, one can either use a solver with lower performance, resulting in larger computation times, or alternatively one seeks an approximation for the unstructured points. A solution to the handling of “dirty” Jacobians is presented, which is implemented in a DAE solver package available freely on the Internet. This novel DAE solver fully exploits the overall structure of the system’s sparsity, without compromising CPU computation time and precision of the results. A numerical comparison with different approaches is given by solving a DAE model representing an existing nonequilibrium distillation column.

To enable decision makers to select sustainable wastewater treatment systems, insight into the sustainability of a wide variety of systems should be provided in a transparent way leaving room for adaptation and interpretation according to the local situation. To provide this insight a structured methodology comparing wastewater treatment systems with respect to sustainability is defined. Similar to life cycle assessment (LCA) three phases can be distinguished: (1) goal and scope definition, (2) inventory analysis, and (3) optimisation and results. In the goal and scope definition we set the system boundaries to include most of the water cycle and part of the food cycle. Furthermore, we defined a multi-disciplinary set of sustainability indicators including technical, economic, environmental, and socio-cultural aspects. In the inventory analysis these sustainability indicators are quantified using simple static models of wastewater unit operations. Selection of unit operations results in a model of a complete wastewater treatment system. In the optimisation phase the decision maker can weigh the different sustainability indicators and select sustainable options through integer programming.