Update README.md formatting and tags

README.md

@@ -2,20 +2,18 @@
 license: apache-2.0
 tags:
 - chemistry
-- pyrolysis
-- biomass
-- neural-network
-- surrogate-model
-- kinetic-modeling
-- thermogravimetry
-- matlab
-- machine-learning
+- pyrolysis-thermochemistry
+- biomass-energy-feedstocks
+- thermodynamics-and-kinetic-modeling
+- physics-informed-neural-networks
+- monte-carlo-bootstrapped-uncertainty
+- autonomous-agent-precision-pyrolysis
 pretty_name: PyroBot Pyrolysis Foundation Models
 ---
 
 # PyroBot Pyrolysis Foundation Models (`bpDNN2Ea` & `bpDNN2Yield`)
 
-This repository contains the core neural network surrogate models of **PyroBot**, an autonomous, agentic framework designed for interpreting, optimizing, and simulating biomass pyrolysis. These foundation surrogates are trained to predict the apparent activation energy ($E_a$) and three-phase product yields (Char, Liquid, Gas) directly from multi-dimensional feedstock properties and process state variables.
+This repository contains the core neural network surrogate models of **PyroBot**, an autonomous, agentic framework designed for interpreting, optimizing, and simulating biomass pyrolysis. These foundation surrogates are trained to predict the apparent activation energy (Ea) and three-phase product yields (Char, Liquid, Gas) directly from multi-dimensional feedstock properties and process state variables.
 
 By utilizing these pre-trained Backpropagation Deep Neural Networks (BP-DNNs) as a fast and accurate surrogate "brain," PyroBot bypasses computationally expensive ab initio or multi-phase CFD simulations, enabling real-time closed-loop autonomous kinetic analysis and thermogravimetric (TG) curve reconstruction.
 
@@ -45,23 +43,23 @@ Each `Results_trained.mat` file contains three core structures:
 ## 🧠 Model Descriptions & Architectures
 
 ### 1. `bpDNN2Ea` (Activation Energy Prediction)
-* **Goal**: Predicts the Apparent Activation Energy ($E_a$, in $\text{kJ/mol}$) as a function of biomass feedstock composition and the instantaneous conversion level ($\alpha$).
-* **Network Topology**: `256` (Input) $\rightarrow$ `42` (Hidden Layer 1) $\rightarrow$ `42` (Hidden Layer 2) $\rightarrow$ `1` (Output).
+* **Goal**: Predicts the Apparent Activation Energy (Ea, in kJ/mol) as a function of biomass feedstock composition and the instantaneous conversion level (α).
+* **Network Topology**: `256` (Input) → `42` (Hidden Layer 1) → `42` (Hidden Layer 2) → `1` (Output).
 * **Input Features (256 Dimensions)**:
-  * **Basic Feedstock Characteristics (19 Dimensions)**: Includes proximate analysis (volatile matter, fixed carbon, ash), ultimate analysis (C, H, O, N, S), and detailed ash compositions ($SiO_2$, $Al_2O_3$, etc.).
-  * **Pyrolysis Progress State (1 Dimension)**: The instantaneous degree of conversion ($\alpha$), ranging from `0.01` to `0.999`.
+  * **Basic Feedstock Characteristics (19 Dimensions)**: Includes proximate analysis (volatile matter, fixed carbon, ash), ultimate analysis (C, H, O, N, S), and detailed ash compositions (SiO₂, Al₂O₃, etc.).
+  * **Pyrolysis Progress State (1 Dimension)**: The instantaneous degree of conversion (α), ranging from `0.01` to `0.999`.
   * **Feedstock Blending/Mixing Features (236 Dimensions)**: Captures complex multi-component feedstock mixtures, geographic source classifications, and blending ratio parameters.
-* **Output**: Apparent Activation Energy $E_a$ ($\text{kJ/mol}$).
+* **Output**: Apparent Activation Energy Ea (kJ/mol).
 
 ### 2. `bpDNN2Yield` (Product Yields Prediction)
 * **Goal**: Predicts the ultimate three-phase pyrolysis yields (Char, Liquid, Gas) under high-temperature conditions.
-* **Network Topology**: `259` (Input) $\rightarrow$ `45` $\rightarrow$ `45` $\rightarrow$ `45` $\rightarrow$ `45` $\rightarrow$ `45` $\rightarrow$ `3` (Outputs).
+* **Network Topology**: `259` (Input) → `45` → `45` → `45` → `45` → `45` → `3` (Outputs).
 * **Input Features (259 Dimensions)**:
   * **Basic Feedstock Characteristics (19 Dimensions)**
   * **Detailed Feedstock Blending/Mixing Features (240 Dimensions)**
 * **Outputs (3 Dimensions)**:
   * Pyrolysis yields (expressed in weight percentages):
-    1. **Char Yield (%)** (used directly as the ultimate residue $w_{\infty}$ for TG scaling).
+    1. **Char Yield (%)** (used directly as the ultimate residue w_inf for TG scaling).
     2. **Liquid/Bio-oil Yield (%)**
     3. **Gas Yield (%)**
 
@@ -71,17 +69,20 @@ Each `Results_trained.mat` file contains three core structures:
 
 In the PyroBot cognitive ecosystem, these two models are combined to reconstruct simulated Thermogravimetric (TG) curves with perfect physical and kinetic consistency:
 
-1. **Mass Loss Curve Platform ($w_{\infty}$)**:
-   The ultimate char yield predicted by `bpDNN2Yield` sets the lower boundary platform ($w_{\infty} = \text{Char Yield}/100$) of the mass-loss profile. For any degree of conversion $\alpha$, the remaining sample weight $w(\alpha)$ is calculated via mass balance:
-   $$w(\alpha) = 100 \times \bigl(1 - \alpha \cdot (1 - w_{\infty})\bigr)\%$$
+1. **Mass Loss Curve Platform (w_inf)**:
+   The ultimate char yield predicted by `bpDNN2Yield` sets the lower boundary platform (w_inf = Char Yield / 100) of the mass-loss profile. For any degree of conversion α, the remaining sample weight w(α) is calculated via mass balance:
+   
+   w(α) = 100 × [1 - α · (1 - w_inf)] %
 
 2. **Kinetic & Temperature Integration**:
-   The conversion-dependent apparent activation energy profile $E_a(\alpha)$ predicted by `bpDNN2Ea` is mapped to an adaptive kinetic solver. The system compares a library of 130 mechanisms (such as diffusion, nucleation, geometrical, and reaction-order models in series/parallel/hybrid modes) and uses a **Genetic Algorithm (GA)** coupled with non-linear optimization (`fmincon`) to solve the Kissinger-Arrhenius equations:
-   $$G(\alpha) = \int_{T_{0}}^{T} \frac{A}{\beta} \exp\left(-\frac{E_a(\alpha)}{R T}\right) dT$$
-   Solving this equation gives the temperature trajectory $T(\alpha)$ and pre-exponential factor $A$.
+   The conversion-dependent apparent activation energy profile Ea(α) predicted by `bpDNN2Ea` is mapped to an adaptive kinetic solver. The system compares a library of 130 mechanisms (such as diffusion, nucleation, geometrical, and reaction-order models in series/parallel/hybrid modes) and uses a **Genetic Algorithm (GA)** coupled with non-linear optimization (`fmincon`) to solve the Kissinger-Arrhenius equations:
+   
+   G(α) = ∫ (A/β) · exp(-Ea(α) / RT) dT
+   
+   Solving this equation gives the temperature trajectory T(α) and pre-exponential factor A.
    
 3. **Synthesis**:
-   Combining $T(\alpha)$ (from the $E_a$ network + kinetic solver) and $w(\alpha)$ (from the Yield network) yields the completed, publication-quality TG curve ($w$ vs. $T$) without requiring numerical interpolation.
+   Combining T(α) (from the Ea network + kinetic solver) and w(α) (from the Yield network) yields the completed, publication-quality TG curve (w vs. T) without requiring numerical interpolation.
 
 ---
 
@@ -100,18 +101,18 @@ For reference, the 19 basic feedstock input characteristics representing the bio
 | **7** | Oxygen (O) | Ultimate analysis of elemental Oxygen | wt.% (dry-basis) |
 | **8** | Nitrogen (N) | Ultimate analysis of elemental Nitrogen | wt.% (dry-basis) |
 | **9** | Sulfur (S) | Ultimate analysis of elemental Sulfur | wt.% (dry-basis) |
-| **10** | $SiO_2$ | Silica percentage in biomass ash | wt.% of ash |
-| **11** | $Al_2O_3$ | Alumina percentage in biomass ash | wt.% of ash |
-| **12** | $Fe_2O_3$ | Iron oxide percentage in biomass ash | wt.% of ash |
-| **13** | $CaO$ | Calcium oxide percentage in biomass ash | wt.% of ash |
-| **14** | $MgO$ | Magnesium oxide percentage in biomass ash | wt.% of ash |
-| **15** | $TiO_2$ | Titanium dioxide percentage in biomass ash | wt.% of ash |
-| **16** | $Na_2O$ | Sodium oxide percentage in biomass ash | wt.% of ash |
-| **17** | $K_2O$ | Potassium oxide percentage in biomass ash | wt.% of ash |
-| **18** | $P_2O_5$ | Phosphorus pentoxide percentage in biomass ash | wt.% of ash |
-| **19** | $SO_3$ | Sulfur trioxide percentage in biomass ash | wt.% of ash |
-
-*Note: For the $E_a$ network, column 20 is the degree of conversion ($\alpha$), and columns 21–256 contain mixed feedstock attributes. For the Yield network, columns 20–259 contain mixed feedstock attributes.*
+| **10** | SiO₂ | Silica percentage in biomass ash | wt.% of ash |
+| **11** | Al₂O₃ | Alumina percentage in biomass ash | wt.% of ash |
+| **12** | Fe₂O₃ | Iron oxide percentage in biomass ash | wt.% of ash |
+| **13** | CaO | Calcium oxide percentage in biomass ash | wt.% of ash |
+| **14** | MgO | Magnesium oxide percentage in biomass ash | wt.% of ash |
+| **15** | TiO₂ | Titanium dioxide percentage in biomass ash | wt.% of ash |
+| **16** | Na₂O | Sodium oxide percentage in biomass ash | wt.% of ash |
+| **17** | K₂O | Potassium oxide percentage in biomass ash | wt.% of ash |
+| **18** | P₂O₅ | Phosphorus pentoxide percentage in biomass ash | wt.% of ash |
+| **19** | SO₃ | Sulfur trioxide percentage in biomass ash | wt.% of ash |
+
+*Note: For the Ea network, column 20 is the degree of conversion (α), and columns 21–256 contain mixed feedstock attributes. For the Yield network, columns 20–259 contain mixed feedstock attributes.*
 
 ---
 
@@ -238,13 +239,11 @@ These models are distributed under the **Apache License 2.0**.
 If you use **PyroBot** or these foundation pyrolysis surrogate networks in your scientific publications or research, please cite our corresponding work:
 
 ```bibtex
-@article{pyrobot2026,
-  title={PyroBot: An Autonomous Agent Framework powered by Foundation Surrogate Deep Neural Networks for Intelligent Biomass Pyrolysis Simulation},
-  author={Tang, Siqi and et al.},
-  journal={Journal of Analytical and Applied Pyrolysis / Fuel / Environmental Science},
-  year={2026},
-  volume={xx},
-  pages={xx},
-  doi={xx}
+@misc{tang2026pyrobot,
+  author = {Tang, Siqi and others},
+  title = {PyroBot_FoundationModel: Pre-trained Deep Neural Network Surrogate Models for Biomass Pyrolysis},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/TANG-Research-Group/PyroBot_FoundationModel}}
 }
 ```