how to maximize by using scipy.optimize.minimize, given -1.0 multiplied by the predicted value

[xw0413happy]

Exchanger Heat Transfer Equation is Q=del_TA0U, in here U is 200 (constant)

The goal is to maximize Q, and to get the optimal value of del_T and A0

Note: Importing libraries and working directory codes are omitted here.

a=pd.read_csv('some.csv')
features=['del_T','A0']
x=a[features]
y=a['Q'] # to extract a series

Define a function

def objective(x,y,sign= -1.0): # to get maximum by using '-1' sign
x1=x[0]
x2=x[1]
x3=200
pred_Q=signx1x2*x3
return pred_Q

Set initial guess

x0=[100,100]

Use scipy.optimize.minimize function to solve the problem

solution=minimize(objective,x0,method='BFGS',args=(x,),tol=1e-06)

Result

print(solution)
fun: -138217962.454975
hess_inv: array([[ 0.49750004, -0.50249996],
[-0.50249996, 0.49750004]])
jac: array([-166266., -166266.])
message: 'Desired error not necessarily achieved due to precision loss.'
nfev: 348
nit: 1
njev: 112
status: 2
success: False
x: array([831.31811737, 831.31811737])

From the above codes,

(1) The simulation processing failed; (2) the x roots are around 831, however, in my csv file, the del_T and A0 value ranges both are 94.00--110.00, the result is way larger than the real dataset, which I don't know why; (3) The maximum Q is -138217962.454975, is a wrong result, too high value.

I bet there is something wrong in my def function codes, but I don't know to fix it, can anybody please help me to figure it out?