Minimal-time bioremediation of natural water resources

We study minimal time strategies for the treatment of pollution of large volumes, such as lakes or natural reservoirs, with the help of an autonomous bioreactor. The control consists in feeding the bioreactor from the resource, the clean output returning to the resource with the same flow rate. We first characterize the optimal policies among constant and feedback controls, under the assumption of a uniform concentration in the resource. In a second part, we study the influence of an inhomogeneity in the resource, considering two measurements points. With the help of the Maximum Principle, we show that the optimal control law is non-monotonic and terminates with a constant phase, contrary to the homogeneous case for which the optimal flow rate is decreasing with time. This study allows the decision makers to identify situations for which the benefit of using non-constant flow rates is significant

Integrating dark and light biohydrogen production strategies: towards the hydrogen economy

Biological methods of hydrogen production are preferable to chemical methods because of the possibility to use sunlight, CO2 and organic wastes as substrates for environmentally benign conversions, under moderate conditions. By combining different microorganisms with different capabilities, the individual strengths of each may be exploited and their weaknesses overcome. Mechanisms of bio-hydrogen production are described and strategies for their integration are discussed. Dual systems can be\ud divided broadly into wholly light-driven systems (with microalgae/cyanobacteria as the 1st stage) and partially light-driven systems (with a dark, fermentative initial reaction). Review and evaluation of published data suggests that the latter type of system holds greater promise for industrial application. This is because the calculated land area required for a wholly light-driven dual system would be too large for either centralised (macro-) or decentralised(micro-) energy generation. The potential contribution to the hydrogen economy of partially light-driven dual systems is overviewed alongside that of other biofuels such as bio-methane and bio-ethanol

125th anniversary review: fuel alcohol: current production and future challenges

Global research and industrial development of liquid transportation biofuels are moving at a rapid pace. This is mainly due to the significant roles played by biofuels in decarbonising our future energy needs, since they act to mitigate the deleterious impacts of greenhouse gas emissions to the atmosphere that are contributors of climate change. Governmental obligations and international directives that mandate the blending of biofuels in petrol and diesel are also acting as great stimuli to this expanding industrial sector. Currently, the predominant liquid biofuel is bioethanol (fuel alcohol) and its worldwide production is dominated by maize-based and sugar cane-based processes in North and South America, respectively. In Europe, fuel alcohol production employs primarily wheat and sugar beet. Potable distilled spirit production and fuel alcohol processes share many similarities in terms of starch bioconversion, fermentation, distillation and co-product utilisation, but there are some key differences. For example, in certain bioethanol fermentations, it is now possible to yield consistently high ethanol concentrations of ~20% (v/v). Emerging fuel alcohol processes exploit lignocellulosic feedstocks and scientific and technological constraints involved in depolymerising these materials and efficiently fermenting the hydrolysate sugars are being overcome. These so-called secondgeneration fuel alcohol processes are much more environmentally and ethically acceptable compared with exploitation of starch and sugar resources, especially when considering utilisation of residual agricultural biomass and biowastes. This review covers both first and second-generation bioethanol processes with a focus on current challenges and future opportunities of lignocellulose-to-ethanol as this technology moves from demonstration pilot-plants to full-scale industrial facilities

Adaptive optimal operation of a parallel robotic liquid handling station

Results are presented from the optimal operation of a fully automated robotic liquid handling station where parallel experiments are performed for calibrating a kinetic fermentation model. To increase the robustness against uncertainties and/or wrong assumptions about the parameter values, an iterative calibration and experiment design approach is adopted. Its implementation yields a stepwise reduction of parameter uncertainties together with an adaptive redesign of reactor feeding strategies whenever new measurement information is available. The case study considers the adaptive optimal design of 4 parallel fed-batch strategies implemented in 8 mini-bioreactors. Details are given on the size and complexity of the problem and the challenges related to calibration of over-parameterized models and scarce and non-informative measurement data. It is shown how methods for parameter identifiability analysis and numerical regularization can be used for monitoring the progress of the experimental campaigns in terms of generated information regarding parameters and selection of the best fitting parameter subset.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Optimal control of fed-batch fermentation processes

Optimisation of a fed-batch fermentation process typically uses the calculus of variations or Pontryagin's maximum principle to determine an optimal feed rate profile. This often results in a singular control problem and an open loop control structure. The singular feed rate is the optimal feed rate during the singular control period and is used to control the substrate concentration in the fermenter at an optimal level. This approach is supported by biological knowledge that biochemical reaction rates are controlled by the environmental conditions in the fermenter; in this case, the substrate concentration. Since an accurate neural net-based on-line estimation of the substrate concentration has recently become available and is currently employed in industry, we are therefore able to propose a method which makes use of this estimation. The proposed method divides the optimisation problem into two parts. First, an optimal substrate concentration profile which governs the biochemical reactions in the fermentation process is determined. Then a controller is designed to track the obtained optimal profile. Since the proposed method determines the optimal substrate concentration profile, the singular control problem is therefore avoided because the substrate concentration appears nonlinearly in the system equations. Also, the process is then operated in closed loop control of the substrate concentration. The proposed method is then called "closed loop optimal control". The proposed closed loop optimal control method is then compared with the open loop optimal feed rate profile method. The comparison simulations from both primary and secondary metabolite production processes show that both methods give similar performance in a case of perfect model while the closed loop optimal control provides better performance than the open loop method in a case of plant/model mismatch. The better performance of the closed loop optimal control is due to an ability to compensate for the modelling errors using feedback

Multi-objective optimization of nonlinear switched time-delay systems in fed-batch process

© 2016 Elsevier Inc.Maximization of productivity and minimization of consumption are two top priorities for biotechnological industry. In this paper, we model a fed-batch process as a nonlinear switched time-delay system. Taking the productivity of target product and the consumption rate of substrate as the objective functions, we present a multi-objective optimization problem involving the nonlinear switched time-delay system and subject to continuous state inequality constraints. To solve the multi-objective optimization problem, we first convert the problem into a sequence of single-objective optimization problems by using convex weighted sum and normal boundary intersection methods. A gradient-based single-objective solver incorporating constraint transcription technique is then developed to solve these single-objective optimization problems. Finally, a numerical example is provided to verify the effectiveness of the numerical solution approach. Numerical results show that the normal boundary intersection method in conjunction with the developed single-objective solver is more favourable than the convex weighted sum method

Bioremediation and biovalorisation of olive-mill wastes

Olive-mill wastes are produced by the industry of olive oil production, which is a very important economic activity, particularly for Spain, Italy and Greece, leading to a large environmental problem of current concern in the Mediterranean basin. There is as yet no accepted treatment method for all the wastes generated during olive oil production, mainly due to technical and economical limitations but also the scattered nature of olive mills across the Mediterranean basin. The production of virgin olive oil is expanding worldwide, which will lead to even larger amounts of olive-mill waste, unless new treatment and valorisation technologies are devised. These are encouraged by the trend of current environmental policies, which favour protocols that include valorisation of the waste. This makes biological treatments of particular interest. Thus, research into different biodegradation options for olive-mill wastes and the development of new bioremediation technologies and/or strategies, as well as the valorisation of microbial biotechnology, are all currently needed. This review, whilst presenting a general overview, focus critically on the most significant recent advances in the various types of biological treatments, the bioremediation technology most commonly applied and the valorisation options, which together will form the pillar for future developments within this fiel