Commercialization of emerging green technologies is essential to improving the sustainability of industrial processes. In practice, it is necessary to match funding sources (e.g., research and development grants, venture capital, etc.) with projects at different maturity levels. Because of inherent uncertainties that characterize and evaluate new technologies, the decision-making process is typically fraught with risk, which can be mitigated with the use of systematic decision support methods. In this work, an optimization model is developed for optimal allocation of funds to a portfolio of innovation projects based on the available funds and different levels of technology maturity. The model is based on source-sink formulation typically used in process integration applications. Each source is a fund of known size and can only be used for projects of a specified minimum return on investment (ROI) and minimum technological readiness level (TRL); each project has an estimated cost, TRL and an ROI range across techno-economic risk scenarios. The model is formulated as a bi-objective mixed integer linear programming (MILP) model, using the conservative and optimistic total portfolio ROI as dual objective functions. The methodology is demonstrated using a pedagogical case study.