Search for: All records

Abstract Industrial ecosystems are coupled with natural systems, which causes the material flow dynamics in the network to be affected by the mechanistic dynamics of each node. However, current material flow dynamics studies do not capture these mechanistic and nonlinear dynamics to evaluate material flows in networks, thus missing its role in designing resilient industrial ecosystems. In this work, we present a methodology to overcome this limitation and model material flow dynamics in a coupled natural‐industrial network by accounting for underlying nonlinear dynamics at each node. We propose a three‐step methodology: first, creating accurate surrogate models using liquid time‐constant (LTC) neural networks to capture node‐specific behavior; second, coupling these individual node models to simulate material flow dynamics in the network; and third, evaluating resilience by measuring the system's ability to maintain production levels under climate stress. Applied to a soybean‐based biodiesel production network in Champaign County, Illinois (2006–2096), our analysis reveals significant vulnerability differences between climate scenarios, with the RCP 8.5 scenario triggering production failures approximately 10 years earlier than RCP 4.5 (2016 vs. 2026), exhibiting higher failure frequency and requiring longer recovery periods. Smaller farms (450 ha) demonstrated substantially higher import dependency, while medium farms (500 ha) reached a critical bifurcation point around 2050 under RCP 8.5, indicating a systemic tipping point. These findings provide insights for policymakers and industrial managers to implement targeted interventions, supply chain diversification, and adaptive management strategies, thereby enhancing system resilience while offering industrial ecology practitioners a methodology for modeling material flow dynamics in a coupled natural‐industrial network. 

Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maintaining interpretability of the underlying mechanistic framework. This work demonstrates the potential for machine learning techniques like SINDy to aid simple mechanistic models in scale-specific predictive accuracy. 

Designing a digital twin will be crucial in developing automation-based future manufacturing systems. The design of digital twins involves data-driven modelling of individual manufacturing units and interactions between the various entities. The goals of future manufacturing units such as zero waste at the plant scale can be formulated as a model-based optimal control problem by identifying the necessary state, control inputs, and manipulated variables. The fundamental assumption of any model-based control scheme is the availability of a reasonable model, and hence, assessing the goodness of the model in terms of stability and sensitivity around the optimal parameter value becomes imperative. This work analyses the data-driven model of an acetaminophen production plant obtained from SINDy, a nonlinear system identification algorithm using sparse identification techniques. Initially, we linearize the system around optimal parameter values and use local stability analysis to assess the stability of the identified model. Further, we use what is known as a conditional sloppiness analysis to identify the sensitivity of the parameters around the optimal parameter values to non-infinitesimal perturbations. The conditional sloppiness analysis will reveal the geometry of the parameter space around the optimal parameter values. This analysis eventually gives valuable information on the robustness of the predictions to the changes in the parameter values. We also identify sensitive and insensitive parameter direction. Finally, we show using numerical simulations that the linearized SINDy model is not good enough for control system design. The pole-placement controller is not robust, and with high probability, the control system becomes unstable to very minimum parameter uncertainty in the gain matrix. 

Continuous manufacturing in pharmaceutical industries has shown great promise to achieve process intensification. To better understand and justify such changes to the current status quo, a technoeconomic analysis of a continuous production must be conducted to serve as a predictive decision-making tool for manufacturers. This paper uses PharmaPy, a custom-made Python-based library developed for pharmaceutical flowsheet analysis, to simulate an annual production cycle for a given active pharmaceutical ingredient (API) of varying production volumes for a batch crystallization system and a continuous mixed suspension, mixed product removal (MSMPR) crystallizer. After each system is optimized, the generalized cost drivers, categorized as capital expenses (CAPEX) or operational expenses (OPEX), are compared. Then, a technoeconomic and sustainability cost analysis is done with the process mass intensity (PMI) as a green metric. The results indicate that while the batch system does have an overall lower cost and better PMI metric at smaller manufacturing scales in comparison with the continuous system, the latter system showed more potential for scaling-up for larger production volumes. 

Industrial networks consist of multiple industrial nodes interacting with each other through material exchanges that support the overall production goal of the network.