Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy.

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks.

PharmaPy: An object-oriented tool for the development of hybrid pharmaceutical flowsheets.

Process design and optimization continue to provide computational challenges as the chemical engineering and process optimization communities seek to address more complex and larger scale applications. Software tools for digital design and flowsheet simulation are readily available for traditional chemical processing applications such as in commodity chemicals and hydrocarbon processing; however, tools for pharmaceutical manufacturing are much less well developed. This paper introduces, PharmaPy, a Python-based modelling platform for pharmaceutical manufacturing systems design and optimization.

Evaluating Manual Sampling Locations for Regulatory and Emergency Response.

Drinking water systems commonly use manual or grab sampling to monitor water quality, identify or confirm issues, and verify that corrective or emergency response actions have been effective. In this paper, the effectiveness of regulatory sampling locations for emergency response is explored. An optimization formulation based on the literature was used to identify manual sampling locations to maximize overall nodal coverage of the system.

Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States.

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (R) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median R below 1 when combined with either stay at home orders (median R 0.

Application of PharmaPy in the digital design of the manufacturing process of an active pharmaceutical ingredient.

Flowsheet design and optimization constitute one of the key challenges in the chemical engineering and process optimization communities. Software tools for digital design and flowsheet simulation are readily available for traditional chemical processing problems such as distillation and hydrocarbon processing, however tools for pharmaceutical manufacturing are much less widely developed. This paper introduces, PharmaPy, a Python-based modelling platform for pharmaceutical facility design and optimization.

Quantifying hydraulic and water quality uncertainty to inform sampling of drinking water distribution systems.

Sampling of drinking water distribution systems is performed to ensure good water quality and protect public health. Sampling also satisfies regulatory requirements and is done to respond to customer complaints or emergency situations. Water distribution system modeling techniques can be used to plan and inform sampling strategies.

Robust state estimation of feeding-blending systems in continuous pharmaceutical manufacturing.

State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions.

Interior-point methods for estimating seasonal parameters in discrete-time infectious disease models.

Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities.

A nonlinear programming approach for estimation of transmission parameters in childhood infectious disease using a continuous time model.

Mathematical models can enhance our understanding of childhood infectious disease dynamics, but these models depend on appropriate parameter values that are often unknown and must be estimated from disease case data. In this paper, we develop a framework for efficient estimation of childhood infectious disease models with seasonal transmission parameters using continuous differential equations containing model and measurement noise. The problem is formulated using the simultaneous approach where all state variables are discretized, and the discretized differential equations are included as constraints, giving a large-scale algebraic nonlinear programming problem that is solved using a nonlinear primal-dual interior-point solver.