## 1 Introduction  
The quest for energy transition to a carbon-neutral economy has increased strategies to end all fossil fuel-related carbon emissions by the middle of the 21 st century [1]. Reducing energy-related carbon dioxide (CO\({}_{2}\)) emissions is a top priority in the struggle against global warming [2]. More efforts are being made towards the realization of cutting carbon emissions and alleviating the consequences of climate change [3; 4; 5]. Nevertheless, the energy industry must be decarbonized in order to achieve the set target. To achieve the carbon neutrality target, renewable energy initiatives have been reported to play a vital role and have the potential to provide up to 90% of the required carbon reductions [1; 6]. In view of these renewable energy sources such as, solar, wind, hydro, tidal, geothermal, and biomass, have been extensively explored and reported to have a significant contribution towards attaining carbon neutrality in the future [7; 8]. Among the various renewable energy sources, biomass has been reported to have great potential to facilitate the actualization of the carbon neutrality target [9]. According to a recent report, if present agricultural practices underwent the anticipated improvements, 1 billion dry tonnes of biomass may be available for energy use per year [10]. That will amount to approximately 14 quadrillion British thermal units annually by 2030. Therefore, biomass has been projected as an important source of sustainable energy in the future [11]. From biomass resources, liquid biofuels like bioethanol and biodiesel can be produced [12]. Besides, the by-product (or waste) from biomass processing can be sustainably utilized to produce value-added products such as hydrogen. The primary byproduct of biodiesel processing is glycerol, with yields of around 10% (_w/w_) [13]. Consequently, around 1.05 pounds of glycerol are generated for every gallon of biodiesel, which could be a huge source of hydrogen production. An extensive review by [PERSON] et al. [14] revealed that hydrogen can be produced from glycerol through different technological pathways such as steam reforming, partial oxidation reforming, auto thermal reforming, aqueous phase reforming, and supercritical water reforming. Some of these processes are energy-intensive and facilitate catalyst deactivation by sintering and carbon deposition. Alternatively, a non-energy-intensive process can be employed to produce hydrogen from glycerol. This process involves the use of thermophilic microorganisms to directly convert glycerol to hydrogen. However, one of the drawbacks of this process is the influence of media compositions on thermophilic biohydrogen production from waste glycerol. To overcome this challenge, authors such as [PERSON] and [PERSON] [15] employed a response surface optimization strategy to optimize the process parameters in order to maximize biohydrogen production. Although optimum conditions were obtained for biohydrogen production, it is uncertain to what extent each of the parameters influence biohydrogen production. This uncertainty can be attributed to the complex bioreaction mechanisms from the conversion of glycerol to the formation of hydrogen. This puzzle can be solved using a data-driven modeling approach by employing machine learning algorithms such as an artificial neural network. Different types of machine-learning algorithms have been employed to model various processes involving hydrogen production. [PERSON] et al. [16] employed Gaussian process regression to model bioethanol production from fountain grass. The findings revealed that an optimized Gaussian process regression successfully modeled the prediction of bioethanol production from fountain grass with an R\({}^{2}\) of 0.993. The pH of the medium was reported to have a significant influence on bioethanol production. In comparison to the support vector machine regression, the Gaussian process regression was found to have a better performance when employed to model hydrogen production from waste effluent from bioprocesses.