## Problem Description and Symbolic Representation

The problem involves maximizing the revenue of a chemical plant that produces two types of compounds, Alnolyte and Blenzoate. The production of these compounds requires the use of an automatic device and a human-operated device, both of which have limited availability.

### Symbolic Variables

Let's define the symbolic variables as follows:
- $x_1$ represents the number of packages of Alnolyte produced.
- $x_2$ represents the number of packages of Blenzoate produced.

### Objective Function

The objective is to maximize the revenue. Given that a package of Alnolyte can be sold for $7 and a package of Blenzoate for $10, the objective function can be represented as:
\[ \text{Maximize:} \quad 7x_1 + 10x_2 \]

### Constraints

1. The automatic device is available for at most 500 minutes. Since it takes 5 minutes to produce a package of Alnolyte and 7 minutes to produce a package of Blenzoate:
\[ 5x_1 + 7x_2 \leq 500 \]

2. The human-operated device is available for at most 500 minutes. Since it takes 4 minutes to produce a package of Alnolyte and 3 minutes to produce a package of Blenzoate:
\[ 4x_1 + 3x_2 \leq 500 \]

3. Non-negativity constraints, as the number of packages produced cannot be negative:
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'packages of Alnolyte'), ('x2', 'packages of Blenzoate')],
    'objective_function': '7*x1 + 10*x2',
    'constraints': [
        '5*x1 + 7*x2 <= 500',
        '4*x1 + 3*x2 <= 500',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Chemical Plant Production")

# Define the variables
x1 = model.addVar(name="Alnolyte", lb=0, vtype=gp.GRB.CONTINUOUS)
x2 = model.addVar(name="Blenzoate", lb=0, vtype=gp.GRB.CONTINUOUS)

# Define the objective function
model.setObjective(7*x1 + 10*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(5*x1 + 7*x2 <= 500, name="Automatic_Device_Constraint")
model.addConstr(4*x1 + 3*x2 <= 500, name="Human_Operated_Device_Constraint")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
    print(f"Maximum revenue: ${7*x1.varValue + 10*x2.varValue:.2f}")
else:
    print("No optimal solution found.")
```