A low cost instrumented wall-cooled packed bed bioreactor resolves spatiotemporal heterogeneity during solid-state fermentation of Aspergillus terreus for lovastatin production

Solid-state fermentation (SSF), particularly when agro-industrial residues are used as substrates, is increasingly recognized as a biotechnological platform compatible with circular economy strategies. Its industrial implementation remains constrained by scale-up challenges associated with bioreactor design and the limited availability of spatially and temporally resolved measurements required to analyze, model, control, and optimize reactor performance. Among the available reactor configurations, wall-cooled packed bed bioreactors are promising for SSF; however, their rational scale-up requires bench scale systems capable of resolving transient fluid dynamics, thermal, mass-transfer and metabolic behavior. Here, we report the design and implementation of a low cost, instrumented bench scale wall-cooled packed bed bioreactor integrated with open source Python code for real time data acquisition. The platform combines Arduino based interfaces for distributed temperature and pressure drop measurements with commercial probes for carbon dioxide, oxygen, and relative humidity monitoring. The temperature and pressure acquisition systems were benchmarked against commercial instruments as baseline, yielding coefficients of determination with relative deviations below 1 %. The integrated platform was subsequently evaluated during solid-state fermentation of Aspergillus terreus for lovastatin production. The system captured axial temperature heterogeneity, the temporal evolution of carbon dioxide production and oxygen consumption, and the progressive increase in pressure drop during fermentation. Final measurements of moisture content, biomass, and lovastatin production revealed marked axial heterogeneity within the packed bed. These results demonstrate that the proposed platform can generate transient datasets needed for reactor analysis, mathematical model development, and future model based scale-up of SSF processes.