docs remove grammar errors

README.md

 # MOO4Modelica
 
-Multi-objective Optimization framework for Modelica.
+A Multi-objective Optimization framework and workflow for Modelica.
 
 GitHub Page: [https://wangzizhe.github.io/MOO4Modelica](https://wangzizhe.github.io/MOO4Modelica)
 
 <img src="./diagrams/MOO4Modelica_framework.png" alt="framework" style="zoom:80%;" />
 
-**Motivation:** Since the paper "*A Multi-objective Optimization Algorithm and Process for Modelica Model*" stated two challenges: 
+**Motivation:** The paper "*A Multi-objective Optimization Algorithm and Process for Modelica Model*" stated two challenges: 
 
 1. the functions for multi-objective optimization are limited in Modelica,
 2. MOO is slow.
 
-I want to design a general framework to solve this two challenges by
+I want to design a general framework to solve these two challenges by
 
 1. coupling Python's MOO frameworks to Modelica using OMPython,
 
@@ -23,7 +23,7 @@ I want to design a general framework to solve this two challenges by
 
 1. **Easy to configure:** All settings and configurations can be defined in `config.py`.
 2. **SoTA algorithms for MOO:** Support different libraries and algorithms.
-3. **Enable using of** **parallel computing**: For accelerated process. 
+3. **Enable use of** **parallel computing**: For accelerated process. 
 4. **Support transformation into feature models**: To better analyze and understand large-scale models.
 5. **Comprehensive debugging system**: Debugging functions for all critical steps.
 
@@ -73,24 +73,24 @@ Zhang, Congcong, et al. "A Multi-objective Optimization Algorithm and Process fo
 
 **Problem:** In the current commercial software based on Modelica model, there are few functions for multi-objective optimization, and the current multi-objective optimization algorithm has the problems of insufficient approximation of the optimal solution set and uneven distribution of the solution set.
 
-**Objective:** In order to solve this problem, Combined with the characteristics of Modelica model and NSGA-II algorithm, this paper proposes a multi-objective optimization algorithm and process for Modelica model.
+**Objective:** To solve this problem, Combined with the characteristics of Modelica model and NSGA-II algorithm, this paper proposes a multi-objective optimization algorithm and process for Modelica model.
 
-**Solution:** This paper provides a multi-objective optimization design process for the current modeling and simulation platform. **The process analyzes the model variables according to ANTLR4 and transform the tree structure**.
+**Solution:** This paper provides a multi-objective optimization design process for the current modeling and simulation platform. **The process analyzes the model variables according to ANTLR4 and transforms the tree structure**.
 
-**Future Work:** MOO takes huge computing resource, so it is slow, the future work would be implement parallel computing to solve this problem.
+**Future Work:** MOO takes huge computing resources, so it is slow, the future work would be to implement parallel computing to solve this problem.
 
 ![Overall Process](./diagrams/overall_process.JPG)
 
 **Zizhe's thoughts:** 
 
-* This paper only provides the idea of the overall process, I can't find it anywhere or reproduce it. Also the ANTLR method seems complicated.
+* This paper only provides the idea of the overall process, I can't find it anywhere or reproduce it. Also, the ANTLR method seems complicated.
 * The future work part in this paper is interesting for me and the authors are not doing it. So I could think about implementing parallel computing.
 
 #### 2. DESA - Optimization of variable structure Modelica models using custom annotations
 
 Bender, Daniel. "DESA: Optimization of variable structure modelica models using custom annotations." *Proceedings of the 7th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools*. 2016.
 
-**Contribution:** The library DESA uses custom annotations to implement the optimization task to the model. Further the model is exported including these meta-information. The DESA optimization tool then allows to set up the optimization task in a Matlab environment and operates the optimization run. In this way the optimization of variable structure models is achieved.
+**Contribution:** The library DESA uses custom annotations to implement the optimization task to the model. Further, the model is exported including this meta-information. The DESA optimization tool then allows to set of the optimization task in a Matlab environment and operates the optimization run. In this way, the optimization of variable structure models is achieved.
 
 ![DESA workflow](./diagrams/workflow.JPG)
 
@@ -104,7 +104,7 @@ Bender, Daniel. "DESA: Optimization of variable structure modelica models using
 
 https://pymoo.org
 
-The framework offers state of the art single- and multi-objective optimization algorithms and many more features related to multi-objective optimization such as visualization and decision making.
+The framework offers state-of-the-art single- and multi-objective optimization algorithms and many more features related to multi-objective optimization such as visualization and decision-making.
 
 #### 1. **DEAP (Distributed Evolutionary Algorithms in Python)**