This work focused on presenting a multivariate inverse artificial neural network (ANNim) by developing two functions coupled to metaheuristic algorithms to increase a parabolic trough collector (PTC). This work aims to provide a new method capable of improving the thermal efficiency of a PTC by determining multiple optimal input variables. At first, two ANN models carried out to predict the PTC thermal efficiency (${\eta }_t$), validated, and compared in detail. For that, six input parameters rim-angle (${\phi }_r$), inlet-temperature ($T_{\mathit{in}}$), ambient-temperature ($T_{\mathit{amb}}$), water volumetric flow rate ($F_w$), direct-solar-radiation ($G_b$) and wind-speed ($V_v$) considered as variables in the input layer. Two non-linear transfer functions (TANSIG and LOGSIG) in the hidden layer, a linear function (PURELIN) in the output layer, and the Levenberg-Marquardt training algorithm were applied. The results showed that both ANN models achieved satisfactory results with a coefficient of determination of 0.9511 and a root mean square error of 0.0193. Then, to get the variable's optimal values: rim-angle, inlet-temperature, and water volumetric flow rate, both ANN models inverted to acquire the multivariable objective function that could be resolved with genetic-algorithms (GA) and particle-swarm-optimization (PSO). The TANSIG function demonstrated better adaptation to the ANNim model by finding all the input variables in a random test with an error of 3.96% with a computational time of 14.39 s applying PSO. The results showed that by using the ANNim methodology, it is feasible to improve the performance of the PTC by optimizing from one, two, and three variables at the same time. In optimizing one variable at a time, it was possible to increase a random test's performance up to 54.78%, 27.62%, and 51.92% by finding the rim-angle inlet-temperature and water volumetric flow rate, respectively. In optimizing two variables simultaneously, it was possible to increase a random test's performance up to 36.73% by finding the appropriate inlet-temperature and water volumetric flow rate. In optimizing three variables simultaneously, it was possible to increase a random experimental test of up to 67.12%. Finally, the new ANNim method proposed may increase the thermal efficiency of a PTC in real-time because of the coupling of metaheuristic algorithms that allow obtaining optimal variables in the shortest possible time. Therefore, it can be a promising and widely used method for optimizing and controlling thermal processes.

这项工作着重于通过开发与元启发式算法耦合的两个函数以增加抛物线槽收集器（PTC）来提出多元逆人工神经网络（ANNim）。这项工作旨在提供一种能够通过确定多个最佳输入变量来提高PTC热效率的新方法。首先，进行了两个ANN模型来预测PTC的热效率（${\eta }_{Ť}$），经过验证和详细比较。为此，六个输入参数rim-angle（${\phi }_{\mathrm{[R}}$），入口温度（${Ť}_{\mathit{在}}$），环境温度（${Ť}_{\mathit{mb}}$），水的体积流量（$F_w$），直接太阳辐射（$G_b$）和风速（$V_v$）视为输入层中的变量。在隐藏层中使用了两个非线性传递函数（TANSIG和LOGSIG），在输出层中使用了线性函数（PURELIN），以及Levenberg-Marquardt训练算法。结果表明，两种人工神经网络模型均获得令人满意的结果，测定系数为0.9511，均方根误差为0.0193。然后，为了获得变量的最佳值：边缘角度，入口温度和水体积流量，将两个ANN模型都求反，以获取可以用遗传算法（GA）和粒子群优化求解的多元目标函数。 （PSO）。TANSIG函数通过在PSO中以14.39 s的计算时间在3.96％的误差下进行的随机测试中找到所有输入变量，证明了其对ANNim模型的更好适应性。结果表明，使用ANNim方法，通过同时优化一个，两个和三个变量来提高PTC的性能是可行的。在一次优化一个变量时，通过找到边角入口温度和水体积流量，可以将随机测试的性能分别提高到54.78％，27.62％和51.92％。通过同时优化两个变量，可以通过找到合适的入口温度和水体积流量将随机测试的性能提高到36.73％。在同时优化三个变量时，可以将随机实验测试提高到67.12％。最后，提出的新的ANNim方法可以实时提高PTC的热效率，这是因为结合了元启发式算法，可以在最短的时间内获得最佳变量。因此，它可能是一种有前途且广泛使用的优化和控制热过程的方法。