A review on machine learning approaches for microalgae cultivation systems.

Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.

Recycling of Spent FCC Catalysts: Conversion of Leached Residues to Zeolite ZSM-5.

The commercial recycling of spent FCC catalyst typically focuses on recovering only 1-3 % of rare-earth elements, with the remaining residues often disposed of in landfills. Here, we present a novel method to close a recycling loop for spent FCC catalyst. The method involves a series of leaching steps: Firstly, the spent catalyst material is leached with HNO3 to remove rare-earth elements such as La; second, solvothermal leaching with HCl removes most of Al and impurities like Fe, Ni and V; finally, a third leaching with H2SO4 removes Ti. The solid residues are then used to synthesize ZSM-5 without the addition of any extra silicon or aluminum sources after mild activation. The impurities in the synthesis gel strongly modify the properties of the zeolite, with ZSM-5 crystals containing higher levels of impurities exhibiting lower crystallinities, surface areas, acidities, cracking activities, as well as larger particle sizes.

Membrane-Assisted Methanol Synthesis Processes and the Required Permselectivity.

Water-selective membrane reactors are proposed in the literature to improve methanol yield for a standalone reactor. However, the methanol productivity is not a precise metric to show the system improvement since, with this approach, we do not consider the amount of energy loss through the undesirable co-permeation of H2, which could otherwise remain on the reaction side at high pressure. In other words, the effectiveness of this new technology should be evaluated at a process flowsheet level to assess its advantages and disadvantages on the overall system performance and, more importantly, to identify the minimum required properties of the membrane. Therefore, an equation-based model for a membrane reactor, developed in Aspen Custom Modeler, was incorporated within the process flowsheet of the methanol plant to develop an integrated process framework to conduct the investigation. We determined the upper limit of the power-saving at 32% by exploring the favorable conditions wherein a conceptual water selective membrane reactor proves more effective. Using these suboptimal conditions, we realized that the minimum required H2O/H2 selectivity is 190 and 970 based on the exergy analysis and overall power requirement, respectively. According to our results, the permselectivity of membranes synthesized for this application in the literature, showing improvements in the one-pass conversion, is well below the minimum requirement when the overall methanol synthesis process flowsheet comes into consideration.