FeOs-CAMPD

基于 FeOs 框架的计算机辅助分子和工艺设计。

该软件包提供执行计算机辅助分子和工艺设计的基础设施。它包括

• NLP/MIQCP 求解器绑定 (Artelys Knitro)
• 实现自定义的外部近似算法以解决结果 MINLP
• 分子表示（组计数（CoMT-CAMD）和分子超结构）
• 属性模型（PC-SAFT 和（异段）gc-PC-SAFT）

以及用于为任意工艺模型运行优化问题的框架。该框架是用 Rust 实现的，可以直接访问或通过 Python 接口访问。

目前，仅通过安装 Artelys Knitro 12 才能解决 CAMPD 问题。非常希望转向开源实现，但由于时间限制，目前尚未立即计划。如果您有兴趣做出贡献，请联系维护者。

安装

Rust

只需将依赖项添加到您的 Cargo.toml

feos-campd = "0.2"

Python

如果您已安装 Rust 编译器，可以使用以下命令直接从源代码构建软件包：

pip install git+https://github.com/feos-org/feos-campd

用法

以下部分展示了在 Python 中使用该框架的方法。在 Rust 和 Python 的语言边界内，API 尽可能保持一致。因此，Rust 中实现的步骤与 API 详细信息相同。有关 API 详细信息，请参阅 文档。

分子表示

# define the molecular representation using a molecule superstructure
molecule = SuperMolecule.alkane(size)
# or a combination of superstructures
molecule = SuperMolecule.non_associating(size)

# or provide a list of molecules to choose from
molecule = CoMTCAMD.from_molecules(list_of_identifiers)
molecule = CoMTCAMD.from_json_molecules(list_of_identifiers)

# or provide an input file with group and structure definitions for CoMT-CAMD
molecule = CoMTCAMD.from_json(list_of_identifiers)

属性模型

# The available property models are compatible with the parameter files in FeOs
property_model = GcPcSaftPropertyModel.from_json([molecule], "sauer2014_hetero.json", "joback1987.json")
property_model = PcSaftPropertyModel.from_json_molecules("gross2001.json", "poling2000.json")
property_model = PcSaftPropertyModel.from_json_groups("sauer2014_homo.json", "joback1987.json")

工艺模型

#To implement a process model in Python, define a class with the following methods:
class ORC:
    # For each process variable: the lower bound, the upper bound, and the initial value
    def variables(self):
        return [[lb_0, ub_0, init_0], [lb_1, ub_1, init_1], ...]
    
    # The number of equality constraints (h(x) = 0)
    def equality_constraints(self):
        return ...
    
    # The number of inequality constraints (g(x) >= 0)
    def inequality_constraints(self):
        return ...
        
    # For given equation of state and process variables x, return the target, and the values of
    # equality and inequality constraints
    def solve(self, eos, x):
        # eos - the equation of state object as used in FeOs, e.g.,
        state = State(eos, temperature=300*KELVIN, pressure=BAR)

        # x - the list of process degrees of freedom, e.g.,
        T_in, p_des, ... = x

        # The function is called with regular Python data types (floats), so the implementation
        # of the process model can be as flexible as desired and involve external function calls
        ...

        return target, equality_constraints, inequality_constraints

优化问题

# combine molecular representation, property model, and process model in an optimization problem
# for a pure component
problem = OptimizationProblem.pure(molecule, property_model, process)

# or a binary mixture
problem = OptimizationProblem.pure([molecule1, molecule2], property_model, process)

# use either of these algorithms
# (The boolean indicates whether the algorithm should update the lower bound of the outer approximation
# True can lead to local optima in non-convex problems)
algorithm = OuterApproximationAlgorithm.DuranGrossmann(True)
algorithm = OuterApproximationAlgorithm.DuranGrossmann(False)
algorithm = OuterApproximationAlgorithm.FletcherLeyffer

# to determine a ranking of the optimal molecules
problem.outer_approximation_ranking(y0, algorithm, num_molecules, "options_NLP.opt", "options_MILP.opt")

分子表示和属性模型可以按照以下方式组合

引用我们

如果您认为 FeOs-torch 对您自己的研究有所帮助，请考虑引用我们的 出版物，这是本库的来源。

@article{rehner2023molecule_superstructures,
  author = {Rehner, Philipp and Schilling, Johannes and Bardow, André},
  title = {Molecule superstructures for computer-aided molecular and process design}
  journal = {Molecular Systems Design & Engineering},
  volume = {8},
  issue = {4},
  number = {12},
  pages = {488-499},
  year = {2023}
}