An in-house Python-based algorithm was developed using simplified molecular-input line-entry specification (SMILES) strings and a dipole moment for estimating the normal boiling point, critical properties, standard enthalpy, vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, viscosity, thermal conductivity, and surface tension of molecules. Normal boiling point, critical properties, and standard enthalpy were estimated by using the Joback group contribution method. Vapor pressure, liquid molar volume, enthalpy of vaporization, heat capacity, and surface tension were estimated by using the Riedel model, Gunn–Yamada model, Clausius–Clapeyron equation, Joback group contribution method, and Brock–Bird model, respectively. Viscosities of liquid and gas were estimated by using the Letsou–Stiel model and the Chapman–Enskog–Brokaw model, respectively. Thermal conductivities of liquid and gas were estimated by using the Sato–Riedel model and Stiel–Thodos model, respectively. Dipole moment was calculated through molecular dynamics simulation using the MMFF94 force field, performed with Avogadro software. A case study was conducted with dihydro-2-methyl-3-furanone (DHMF), 2-furaldehyde diethyl acetal (FDA), 1,1-diethoxy-3-methyl butane (DEMB), glutathione (GSH), vitamin B5 (VITB5), homocysteine (HCYS), and O-acetyl-l-homoserine (AH), which are not present in the existing property database. Cross-validation indicated that the developed Python-based algorithm provided pure component model parameters nearly identical with those obtained with the Aspen Property Constant Estimation System (PCES) method, except for the enthalpy of vaporization. The parameters for estimating the enthalpy of vaporization using the current Python-based algorithm accurately represented the behavior of the actual substances, as determined using the Clausius–Claperyon equation. This Python-based algorithm provides a detailed and clear reference for estimating pure property parameters.

A major challenge in bioprocess simulation is the lack of physical and chemical property databases for biochemicals. A Python-based algorithm was developed for estimating the nonrandom two-liquid (NRTL) model parameters of aqueous binary systems in a straightforward manner from simplified molecular-input line-entry specification (SMILES) strings of substances in a system. This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder-Mead method (NMM). The algorithm was applied to aqueous, binary mixture systems composed of 37 common biochemical substances such as amino acids, organic acids, and sugars. The obtained NRTL parameters were compared with those from Aspen Plus, a commercial software, which has an equivalent function for estimating the NRTL parameters. The percentage mean absolute residuals of the activity coefficients obtained using DEA, NMM, and the parameter estimation tool in Aspen Plus were in the ranges of 0.05-16.69, 0.05-16.69, and 0.09-326.77%, respectively. This in-house algorithm will be helpful for obtaining more accurate NRTL parameters in a timely manner and will facilitate the simulation of biochemical processes for process optimization, energy consumption estimation, and life cycle assessment.

In this study, we developed a Python-based open-source algorithm compatible with the aqueous physical property models provided in the electrolyte templates of AspenTech software. To validate the accuracy of the model, the results obtained from the proposed algorithm were compared to experimental data for 37 binary aqueous mixture systems covering properties such as density, heat capacity, viscosity, and thermal conductivity. The input variables included results from our previous research on pure component property prediction and the nonrandom two-liquid (NRTL) model parameters based on the UNIFAC model simulations. This open-source algorithm is compatible with AspenTech software. The mean absolute percentage errors (MAPE) for density, heat capacity, viscosity, and thermal conductivity were 2.88, 0.355, 12.1, and 10.1%, respectively. In the case of density and viscosity, the actual data trends could not be accurately reflected under high-concentration conditions for certain substances. In addition, it was confirmed that inaccurate predictions of the viscosity and thermal conductivity in the commercial-scale falling-film evaporator simulation for l-valine production led to inaccurate predictions of the overall heat transfer coefficient. Therefore, caution is required when predicting missing property parameters using this approach as significant errors may occur. Nevertheless, this algorithm can provide an initial parameter value for property models that are not included in existing databases without any commercial package.