Realistic model of a solid substrate fermentation packed-bed pilot bioreactor

Digital Proxy of a Bio-Reactor (DIYBOT) combines sensor data and data analytics to improve greywater treatment and wastewater management systems

Technologies to treat wastewater in decentralized systems are critical for sustainable development. Bioreactors are suitable for low-energy removal of inorganic and organic compounds, particularly for non-potable applications where a small footprint is required. One of the main problems associated with bioreactor use is sporadic spikes of chemical toxins, including nanoparticles. Here, we describe the development of DIYBOT (Digital Proxy of a Bio-Reactor), which enables remote monitoring of bioreactors and uses the data to inform decisions related to systems management. To test DIYBOT, a household-scale membrane aerated bioreactor with real-time water quality sensors was used to treat household greywater simulant. After reaching steady-state, silver nanoparticles (AgNP) representative of the mixture found in laundry wastewater were injected into the system to represent a chemical contamination. Measurements of carbon metabolism, effluent water quality, biofilm sloughing rate, and microbial diversity were characterized after nanoparticle exposure. Real-time sensor data were analyzed to reconstruct phase-space dynamics and extrapolate a phenomenological digital proxy to evaluate system performance. The management implication of the stable-focus dynamics, reconstructed from observed data, is that the bioreactor self-corrects in response to contamination spikes at AgNP levels below 2.0 mg/L. DIYBOT may help reduce the frequency of human-in-the-loop corrective management actions for wastewater processing.

Mathematical modeling of Kluyveromyces marxianus growth in solid-state fermentation using a packed-bed bioreactor

This work investigated the growth of Kluyveromyces marxianus NRRL Y-7571 in solid-state fermentation in a medium composed of sugarcane bagasse, molasses, corn steep liquor and soybean meal within a packed-bed bioreactor. Seven experimental runs were carried out to evaluate the effects of flow rate and inlet air temperature on the following microbial rates: cell mass production, total reducing sugar and oxygen consumption, carbon dioxide and ethanol production, metabolic heat and water generation. A mathematical model based on an artificial neural network was developed to predict the above-mentioned microbial rates as a function of the fermentation time, initial total reducing sugar concentration, inlet and outlet air temperatures. The results showed that the microbial rates were temperature dependent for the range 27-50 degrees C. The proposed model efficiently predicted the microbial rates, indicating that the neural network approach could be used to simulate the microbial growth in SSF.

Exploitation of biological wastes for the production of value-added products under solid-state fermentation conditions

Biological wastes contain several reusable substances of high value such as soluble sugars and fibre. Direct disposal of such wastes to soil or landfill causes serious environmental problems. Thus, the development of potential value-added processes for these wastes is highly attractive. These biological wastes can be used as support-substrates in solid-state fermentation (SSF) to produce industrially relevant metabolites with great economical advantage. In addition, it is an environmentally friendly method of waste management. This paper reviews the reutilization of biological wastes for the production of value-added products using the SSF technique.