Web Applications

Biomass Utilization Superstructure

Biomass can be converted to fuels, fuel additives, and chemicals via multiple production strategies, where each strategy starts from a biomass feedstock and through a series of conversion technologies leads to the production of one or more target fuels. Despite the large number of system-level analyses in the literature, there are limited tools available for (i) the identification (and assessment) of new biofuel production strategies, and (ii) the simultaneous assessment and comparison of alternative strategies. To this end, we developed an online tool, based on a process optimization model, called biomass utilization superstructure (BUS), which allows a user to generate a superstructure of biomass conversion technologies and assess all embedded strategies. The details of the optimization model can be found in Kim et al. (2013) while its features are described in Ng et al. (2018). BUS can be accessed here. In addition to researchers working in the area of biofuels, the tool can be used by instructors to illustrate the ideas underpinning superstructure-based process synthesis.   

Example of BUS

Figure 1. Screenshot of an optimal strategy obtained using BUS web application.

References

Kim J, Sen SM, Maravelias CT. An Optimization-Based Assessment Framework for Biomass-to-Fuels Conversion Strategies. Energy and Environmental Science, 6(4), 1093-1104, 2013.  

Ng RTL, Patchin S, Wu W, Sheth N, Maravelias CT. An Optimization-based Web Application for Synthesis and Analysis of Biomass-to-fuels Strategies. Biofuels, Bioproducts & Biorefining, 12(2), 170-176, 2018.

Chemical Production Scheduler

Manufacturing in multiproduct facilities is becoming increasingly common in the chemical industry due to a continual movement of the sector toward product customization and diversification. Multiproduct facilities offer flexibility and allow efficient asset utilization but require sophisticated production scheduling methods, because multiple shared resources must be efficiently allocated among competing tasks. The allocation of resources to tasks is precisely the goal of scheduling, a short-term (or operational) problem, solved on a daily to weekly basis. In general, scheduling problems can be represented as a mixed integer programming models, which becomes difficult to solve with increase in problem size. Over the years, we have developed several solution techniques (e.g., tightening constraints, reformulation, and discrete continuous algorithm) to address this challenge (Velez and Maravelias, 2014). To facilitate the application of these methods, we developed a web application called chemical production scheduler (CPS). The application is based on state task network representation, allowing a user to define the problem using a graphical user interface and offers the flexibility of choosing different optimization models and solution methods. CPS can be accessed here.  

Example of CPS

Figure 2. Screenshot of CPS web application.

References

Velez S, Maravelias CT. Advances in Mixed-Integer Programming Methods for Chemical Production Scheduling. Annual Review in Chemical and Biomolecular Engineering, 5, 97-121, 2014.